Written by Aunoy Poddar July 21st, 2022
current_file <- rstudioapi::getActiveDocumentContext()$path
output_file <- stringr::str_replace(current_file, '.Rmd', '.R')
knitr::purl(current_file, output = output_file)
file.edit(output_file)
library(Seurat)
Registered S3 methods overwritten by 'htmltools':
method from
print.html tools:rstudio
print.shiny.tag tools:rstudio
print.shiny.tag.list tools:rstudio
Registered S3 method overwritten by 'data.table':
method from
print.data.table
Registered S3 method overwritten by 'htmlwidgets':
method from
print.htmlwidget tools:rstudio
Attaching SeuratObject
library(tictoc)
library(ggplot2)
library(patchwork)
library(pheatmap)
library(RColorBrewer)
library(tidyverse)
Registered S3 methods overwritten by 'dbplyr':
method from
print.tbl_lazy
print.tbl_sql
── Attaching packages ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────── tidyverse 1.3.1 ──
✔ tibble 3.1.8 ✔ dplyr 1.0.8
✔ tidyr 1.2.0 ✔ stringr 1.4.0
✔ readr 2.1.2 ✔ forcats 0.5.1
✔ purrr 0.3.4
── Conflicts ────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag() masks stats::lag()
library(gridExtra)
Attaching package: ‘gridExtra’
The following object is masked from ‘package:dplyr’:
combine
library(png)
library(cowplot)
Attaching package: ‘cowplot’
The following object is masked from ‘package:patchwork’:
align_plots
library(magick)
Linking to ImageMagick 6.9.10.23
Enabled features: fontconfig, freetype, fftw, lcms, pango, webp, x11
Disabled features: cairo, ghostscript, heic, raw, rsvg
Using 80 threads
library(scales)
Attaching package: ‘scales’
The following object is masked from ‘package:purrr’:
discard
The following object is masked from ‘package:readr’:
col_factor
data_dir = '/home/aunoy/st/arc_profiling/st_analysis/hand_annotated_data/rethresholded'
meta_dir = '/home/aunoy/st/arc_profiling/st_analysis/hand_annotated_data/overlay'
output_dir_plot = '/home/aunoy/st/arc_profiling/st_analysis/results/plots'
output_dir_tbls = '/home/aunoy/st/arc_profiling/st_analysis/results/tables'
df_408 = data.frame()
for (file_name in list.files(data_dir)){
print(file_name)
if(grepl('164', file_name)){
next
}
#if(grepl('408_TC', file_name) | grepl('408_vMS', file_name)){
# next
#}
df_to_append <- read.table(file.path(data_dir, file_name), sep = ',', header = TRUE)
while(length(ind <- which(df_to_append$Image.Name == "")) > 0){
df_to_append$Image.Name[ind] <- df_to_append$Image.Name[ind -1]
}
colnames(df_to_append) <- toupper(colnames(df_to_append))
df_to_append <- df_to_append %>%
mutate(area = strsplit(file_name, '.csv')[[1]])
## Add relative_XY_position
if(!is_empty(df_408)){
df_to_append <- df_to_append %>%
dplyr::select(colnames(df_408))
}
df_408 <- rbind(df_408, df_to_append)
}
[1] "164_CC.csv"
[1] "164_MS_CC.csv"
[1] "164_MS_TC.csv"
[1] "164_TC.csv"
[1] "408_CC.csv"
[1] "408_dMS_TC.csv"
[1] "408_MS_CC.csv"
[1] "408_TC.csv"
[1] "408_vMS_TC.csv"
df_408$IMAGE.NAME = unlist(lapply(df_408$IMAGE.NAME, gsub, pattern='_Cluster', replacement=''))
df_408$IMAGE.NAME = unlist(lapply(df_408$IMAGE.NAME, gsub, pattern='[*]', replacement=''))
df_408$IMAGE.NAME = unlist(lapply(df_408$IMAGE.NAME, gsub, pattern='X', replacement=''))
df_408$IMAGE.NAME = unlist(lapply(df_408$IMAGE.NAME, gsub, pattern='L2_', replacement='L2-'))
df_408$IMAGE.NAME = unlist(lapply(df_408$IMAGE.NAME, gsub, pattern='-L2', replacement='_L2'))
df_408$IMAGE.NAME = unlist(lapply(df_408$IMAGE.NAME, gsub, pattern='Tc_12', replacement='TC_12'))
## Missing
df_408 = df_408[df_408$IMAGE.NAME != 'Layer1', ]
df_408 = df_408[df_408$IMAGE.NAME != 'TC_1', ]
df_408 = df_408[df_408$IMAGE.NAME != 'TC_18', ]
df_408 = df_408[df_408$IMAGE.NAME != 'TC_19', ]
#df_408$IMAGE.NAME = toupper(df_408$IMAGE.NAME)
unique(df_408$IMAGE.NAME)
[1] "CC_Cortical1" "CC_Cortical2" "CC_L2-1" "CC_L2-2" "CC_L2-3" "TC_2" "TC_3" "TC_4" "TC_5" "TC_6"
[11] "TC_7" "TC_8" "TC_9" "TC_10" "CC_4" "CC_5" "CC_6" "CC_7" "CC_8" "CC_9"
[21] "CC_10" "CC_11" "CC_12" "TC_16" "TC_17" "TC_20" "TC_11" "TC_12" "TC_13" "TC_14"
[31] "TC_15"
unique(df_408$IMAGE.NAME)
[1] "CC_Cortical1" "CC_Cortical2" "CC_L2-1" "CC_L2-2" "CC_L2-3" "TC_2" "TC_3" "TC_4" "TC_5" "TC_6"
[11] "TC_7" "TC_8" "TC_9" "TC_10" "CC_4" "CC_5" "CC_6" "CC_7" "CC_8" "CC_9"
[21] "CC_10" "CC_11" "CC_12" "TC_16" "TC_17" "TC_20" "TC_11" "TC_12" "TC_13" "TC_14"
[31] "TC_15"
images_ordered = c('TC_20', 'TC_17', 'TC_16', 'TC_15', 'TC_14', 'TC_13', 'TC_12', 'TC_11', 'TC_10', 'TC_9', 'TC_8', 'TC_7', 'TC_6', 'TC_5',
'TC_4', 'TC_3', 'TC_2', 'CC_4', 'CC_5', 'CC_6', 'CC_7', 'CC_8', 'CC_9', 'CC_10', 'CC_11', 'CC_12', 'CC_L2-3', 'CC_L2-2', 'CC_L2-1', 'CC_Cortical1', 'CC_Cortical2')
x_horz = 1:length(images_ordered) * 35
y_horz = rep(0, length(images_ordered))
horz_embedding = data.frame()
df_408$X_horz = -1
df_408$Y_horz = -1
IMAGE_SIZE = 1024
## This is the size of an image in the global coordinate space
IMAGE_LEN = 25
images = list.files(meta_dir)
for(i in 1:length(images_ordered)){
image_name = images_ordered[i]
print(image_name)
split_names = strsplit(image_name, '_')
cortex = toupper(split_names[[1]][1])
number = split_names[[1]][2]
number_csv = paste0('_', number, '.csv')
filename = images[grepl(cortex, images) & grepl(number_csv, images) & grepl('408', images)]
coordinates = read.table(file.path(meta_dir, filename), sep = ',', header = TRUE)
## checked already that lists are equal, missing 1, 18, 19 for now, layer 1 and others
## so this is a little tricky, so need to get it right
## Remember, it is the top right that the coordinate is coming from, but
## the bottom right is the new coordinate space.
## so first when we get the original coordinate space, to set to relative
## of bottom would be the same X, but 1024 - Y
## push out the coordinates for better visualization
#x_repelled <- (512 - coordinates$X_Coordinate_In_pixels)
df_408[df_408$IMAGE.NAME == image_name, 'X_horz'] = (coordinates$X_Coordinate_In_pixels /
IMAGE_SIZE * IMAGE_LEN) + y_horz[i]
df_408[df_408$IMAGE.NAME == image_name, 'Y_horz'] = ((1024-coordinates$Y_Coordinate_In_pixels) /
IMAGE_SIZE * IMAGE_LEN) + x_horz[i]
}
[1] "TC_20"
[1] "TC_17"
[1] "TC_16"
[1] "TC_15"
[1] "TC_14"
[1] "TC_13"
[1] "TC_12"
[1] "TC_11"
[1] "TC_10"
[1] "TC_9"
[1] "TC_8"
[1] "TC_7"
[1] "TC_6"
[1] "TC_5"
[1] "TC_4"
[1] "TC_3"
[1] "TC_2"
[1] "CC_4"
[1] "CC_5"
[1] "CC_6"
[1] "CC_7"
[1] "CC_8"
[1] "CC_9"
[1] "CC_10"
[1] "CC_11"
[1] "CC_12"
[1] "CC_L2-3"
[1] "CC_L2-2"
[1] "CC_L2-1"
[1] "CC_Cortical1"
[1] "CC_Cortical2"
rownames(df_408) = 1:nrow(df_408)
jy_408 = df_408 %>%
dplyr::select(-c(area, IMAGE.NAME, X_horz, Y_horz)) %>%
t() %>%
CreateSeuratObject()
jy_408 <- NormalizeData(jy_408, scale.factor = 1e5) ###
Performing log-normalization
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
normed = GetAssayData(jy_408, slot = 'data')
normed[normed < 3] = 0
jy_408 <- SetAssayData(jy_408, slot = 'data', normed)
jy_408 <- FindVariableFeatures(jy_408, selection.method = "vst")
Calculating gene variances
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating feature variances of standardized and clipped values
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
all.genes <- rownames(jy_408)
jy_408 <- ScaleData(jy_408, features = all.genes)
Centering and scaling data matrix
|
| | 0%
|
|======================================================================================================================================================| 100%
jy_408 <- RunPCA(jy_408, approx = FALSE)
Warning: Requested number is larger than the number of available items (32). Setting to 32.
Warning: Requested number is larger than the number of available items (32). Setting to 32.
Warning: Requested number is larger than the number of available items (32). Setting to 32.
Warning: Requested number is larger than the number of available items (32). Setting to 32.
Warning: Requested number is larger than the number of available items (32). Setting to 32.
PC_ 1
Positive: VIP, ASCL1, CXCR4, RELN, SATB2, MAF1, KIA0319, EMX1, GAD1, CXCL12
PAX6, DLX2, SST, PROX1, LHX6, NCAM1
Negative: DCX, TBR1, LRP8, SCGN, COUPTF2, EGFR, CXCL14, TSHZ1, GSX2, EOMES
SP8, NKX2.1, CALB2, CXCR7, DCDC2, VLDLR
PC_ 2
Positive: TBR1, EOMES, KIA0319, DCDC2, LRP8, CALB2, DCX, SATB2, CXCL12, EGFR
EMX1, ASCL1, COUPTF2, PAX6, RELN, CXCR4
Negative: MAF1, TSHZ1, NKX2.1, SST, GAD1, DLX2, PROX1, SP8, GSX2, VIP
SCGN, LHX6, VLDLR, CXCL14, CXCR7, NCAM1
PC_ 3
Positive: COUPTF2, DLX2, PROX1, GAD1, CXCR4, LHX6, LRP8, TSHZ1, CXCL14, EOMES
NKX2.1, SP8, CALB2, CXCL12, DCDC2, VLDLR
Negative: SATB2, RELN, MAF1, PAX6, ASCL1, SCGN, SST, EMX1, GSX2, NCAM1
DCX, KIA0319, VIP, EGFR, CXCR7, TBR1
PC_ 4
Positive: NCAM1, TSHZ1, VLDLR, CXCL14, PROX1, EGFR, CXCR7, DCX, ASCL1, DCDC2
CALB2, PAX6, GSX2, SCGN, EOMES, KIA0319
Negative: LHX6, DLX2, LRP8, SP8, VIP, SST, COUPTF2, NKX2.1, TBR1, CXCR4
EMX1, RELN, CXCL12, MAF1, GAD1, SATB2
PC_ 5
Positive: GSX2, SP8, NKX2.1, SCGN, COUPTF2, CXCL14, EGFR, EMX1, TSHZ1, CALB2
CXCL12, TBR1, PAX6, SATB2, MAF1, KIA0319
Negative: SST, CXCR4, DCDC2, GAD1, PROX1, EOMES, DLX2, VIP, DCX, VLDLR
RELN, NCAM1, LHX6, ASCL1, CXCR7, LRP8
jy_408 <- FindNeighbors(jy_408, dims = 1:30)
Computing nearest neighbor graph
Computing SNN
jy_408 <- FindClusters(jy_408, resolution = 1.5)
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 1013
Number of edges: 35954
Running Louvain algorithm...
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Maximum modularity in 10 random starts: 0.5629
Number of communities: 12
Elapsed time: 0 seconds
jy_408 <- RunUMAP(jy_408, dims = 1:30)
Warning: The default method for RunUMAP has changed from calling Python UMAP via reticulate to the R-native UWOT using the cosine metric
To use Python UMAP via reticulate, set umap.method to 'umap-learn' and metric to 'correlation'
This message will be shown once per session
17:52:20 UMAP embedding parameters a = 0.9922 b = 1.112
17:52:20 Read 1013 rows and found 30 numeric columns
17:52:20 Using Annoy for neighbor search, n_neighbors = 30
17:52:20 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
17:52:20 Writing NN index file to temp file /tmp/RtmpKuWa3v/filebb88168a050d
17:52:20 Searching Annoy index using 1 thread, search_k = 3000
17:52:20 Annoy recall = 100%
17:52:21 Commencing smooth kNN distance calibration using 1 thread
17:52:21 Initializing from normalized Laplacian + noise
17:52:21 Commencing optimization for 500 epochs, with 38030 positive edges
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
17:52:22 Optimization finished
DimPlot(jy_408, reduction = "umap", group.by = 'seurat_clusters') + NoAxes()
hcoords = df_408 %>% dplyr::select(c('X_horz', 'Y_horz')) %>% as.matrix()
colnames(hcoords) <- c('pixel_1', 'pixel_2')
jy_408[["H"]] <- CreateDimReducObject(embeddings = hcoords, key = "pixel_", assay = DefaultAssay(jy_408))
df_164 = data.frame()
for (file_name in list.files(data_dir)){
print(file_name)
if(grepl('408', file_name)){
next
}
#if(grepl('408_TC', file_name) | grepl('408_vMS', file_name)){
# next
#}
df_to_append <- read.table(file.path(data_dir, file_name), sep = ',', header = TRUE)
while(length(ind <- which(df_to_append$Image.Name == "")) > 0){
df_to_append$Image.Name[ind] <- df_to_append$Image.Name[ind -1]
}
colnames(df_to_append) <- toupper(colnames(df_to_append))
df_to_append <- df_to_append %>%
mutate(area = strsplit(file_name, '.csv')[[1]])
## Add relative_XY_position
if(!is_empty(df_164)){
df_to_append <- df_to_append %>%
dplyr::select(colnames(df_164))
}
df_164 <- rbind(df_164, df_to_append)
}
[1] "164_CC.csv"
[1] "164_MS_CC.csv"
[1] "164_MS_TC.csv"
[1] "164_TC.csv"
[1] "408_CC.csv"
[1] "408_dMS_TC.csv"
[1] "408_MS_CC.csv"
[1] "408_TC.csv"
[1] "408_vMS_TC.csv"
df_164$IMAGE.NAME = unlist(lapply(df_164$IMAGE.NAME, gsub, pattern='CC-', replacement='CC_'))
df_164$IMAGE.NAME = unlist(lapply(df_164$IMAGE.NAME, gsub, pattern='[*]', replacement=''))
df_164$IMAGE.NAME = unlist(lapply(df_164$IMAGE.NAME, gsub, pattern='X', replacement=''))
df_164$IMAGE.NAME = unlist(lapply(df_164$IMAGE.NAME, gsub, pattern='L2', replacement='CC_L2'))
df_164$IMAGE.NAME = unlist(lapply(df_164$IMAGE.NAME, gsub, pattern='L2_', replacement='L2-'))
df_164$IMAGE.NAME = unlist(lapply(df_164$IMAGE.NAME, gsub, pattern='-L2', replacement='_L2'))
tc_cortical_names_bad = df_164[grepl('TC', df_164$area) & grepl('Cortical', df_164$IMAGE.NAME), 'IMAGE.NAME']
df_164[grepl('TC', df_164$area) & grepl('Cortical', df_164$IMAGE.NAME), 'IMAGE.NAME'] = unlist(lapply(tc_cortical_names_bad, gsub, pattern='Cort', replacement='TC_Cort'))
cc_cortical_names_bad = df_164[grepl('CC', df_164$area) & grepl('Cortical', df_164$IMAGE.NAME), 'IMAGE.NAME']
df_164[grepl('CC', df_164$area) & grepl('Cortical', df_164$IMAGE.NAME), 'IMAGE.NAME'] = unlist(lapply(cc_cortical_names_bad, gsub, pattern='Cort', replacement='CC_Cort'))
#df_164$IMAGE.NAME = unlist(lapply(df_164$IMAGE.NAME, gsub, pattern='Tc_12', replacement='TC_12'))
## Missing
#df_164 = df_164[df_164$IMAGE.NAME != 'Layer1', ]
#df_164 = df_164[df_164$IMAGE.NAME != 'TC_1', ]
#df_164 = df_164[df_164$IMAGE.NAME != 'TC_18', ]
#df_164 = df_164[df_164$IMAGE.NAME != 'TC_19', ]
#df_164$IMAGE.NAME = toupper(df_164$IMAGE.NAME)
unique(df_164$IMAGE.NAME)
[1] "CC_8" "CC_10" "CC_Cortical1" "CC_Cortical2" "CC_L2-1" "CC_L2-2" "CC_L2-3" "CC_2" "CC_3" "CC_4"
[11] "CC_5" "CC_6" "CC_7" "CC_9" "TC_1" "TC_2" "TC_3" "TC_4" "TC_5" "TC_6"
[21] "TC_7" "TC_8" "TC_9" "TC_10" "TC_Cortical1" "TC_Cortical2" "TC_Cortical3"
image_names = unique(df_164$IMAGE.NAME)
# Preset these variables to negative values so I can easily check if they were updated later
df_164$X = -1
df_164$Y = -1
# set some normalization variables
## This is the size of the image when the pixel values are taken from top left down
IMAGE_SIZE = 1024
## This is the size of an image in the global coordinate space
IMAGE_LEN = 32
TC_IMAGE_HEIGHT = 410
TC_IMAGE_WIDTH = 446
CC_IMAGE_HEIGHT= 422
CC_IMAGE_WIDTH = 214
# Load the dataframe with global and relative coordinates
img_cords = read.table(file.path(meta_dir, '164_pixel_coordinates.csv'), sep = ',', header = TRUE)
images = list.files(meta_dir)
for(image_name in image_names){
if(grepl('408', image_name)){
next
}
print(image_name)
split_names = strsplit(image_name, '_')
cortex = toupper(split_names[[1]][1])
number = split_names[[1]][2]
number_csv = paste0('_', number, '.csv')
filename = images[grepl(cortex, images) & grepl(number_csv, images) & grepl('164', images)]
coordinates = read.table(file.path(meta_dir, filename), sep = ',', header = TRUE)
if(image_name == "CC_L2-1"){
coordinates = coordinates[c(1:37, 39:nrow(coordinates)), ]
}
if(cortex == 'CC'){
print(paste('cc', filename, image_name))
## So if CC, we add the coordinates for TC_1 to overall image coordinates
x_adj = img_cords[img_cords$Name == 'TC_1', 'x'] +
img_cords[img_cords$Name == 'G_CC1_to_TC1', 'x']
## Start from bottom, add the height, subtract TC_1 height, and then global CC1 to TC1
y_adj = TC_IMAGE_HEIGHT - img_cords[img_cords$Name == 'TC_1', 'y'] +
img_cords[img_cords$Name == 'G_CC1_to_TC1', 'y'] + CC_IMAGE_HEIGHT
}else{
print(paste('tc', filename, image_name))
x_adj = 0
y_adj = TC_IMAGE_HEIGHT
}
## So don't do repelled for now
#x_repelled <- (512 - coordinates$X_Coordinate_In_pixels)
## so the resized x distance is from left, so just add to the box location and adj
df_164[df_164$IMAGE.NAME == image_name, 'X'] = (coordinates$X_Coordinate_In_pixels /
IMAGE_SIZE * IMAGE_LEN) +
img_cords[img_cords$Name == image_name, 'x'] + x_adj
## resized y distance
df_164[df_164$IMAGE.NAME == image_name, 'Y'] = y_adj - img_cords[img_cords$Name == image_name, 'y'] -
(coordinates$Y_Coordinate_In_pixels / IMAGE_SIZE *
IMAGE_LEN)
}
[1] "CC_8"
[1] "cc 164_CC_ROI_CC_8.csv CC_8"
[1] "CC_10"
[1] "cc 164_CC_ROI_CC_10.csv CC_10"
[1] "CC_Cortical1"
[1] "cc 164_CC_ROI_CC_Cortical1.csv CC_Cortical1"
[1] "CC_Cortical2"
[1] "cc 164_CC_ROI_CC_Cortical2.csv CC_Cortical2"
[1] "CC_L2-1"
[1] "cc 164_CC_ROI_CC_L2-1.csv CC_L2-1"
[1] "CC_L2-2"
[1] "cc 164_CC_ROI_CC_L2-2.csv CC_L2-2"
[1] "CC_L2-3"
[1] "cc 164_CC_ROI_CC_L2-3.csv CC_L2-3"
[1] "CC_2"
[1] "cc 164_CC_ROI_CC_2.csv CC_2"
[1] "CC_3"
[1] "cc 164_CC_ROI_CC_3.csv CC_3"
[1] "CC_4"
[1] "cc 164_CC_ROI_CC_4.csv CC_4"
[1] "CC_5"
[1] "cc 164_CC_ROI_CC_5.csv CC_5"
[1] "CC_6"
[1] "cc 164_CC_ROI_CC_6.csv CC_6"
[1] "CC_7"
[1] "cc 164_CC_ROI_CC_7.csv CC_7"
[1] "CC_9"
[1] "cc 164_CC_ROI_CC_9.csv CC_9"
[1] "TC_1"
[1] "tc 164_TC_ROI_TC_1.csv TC_1"
[1] "TC_2"
[1] "tc 164_TC_ROI_TC_2.csv TC_2"
[1] "TC_3"
[1] "tc 164_TC_ROI_TC_3.csv TC_3"
[1] "TC_4"
[1] "tc 164_TC_ROI_TC_4.csv TC_4"
[1] "TC_5"
[1] "tc 164_TC_ROI_TC_5.csv TC_5"
[1] "TC_6"
[1] "tc 164_TC_ROI_TC_6.csv TC_6"
[1] "TC_7"
[1] "tc 164_TC_ROI_TC_7.csv TC_7"
[1] "TC_8"
[1] "tc 164_TC_ROI_TC_8.csv TC_8"
[1] "TC_9"
[1] "tc 164_TC_ROI_TC_9.csv TC_9"
[1] "TC_10"
[1] "tc 164_TC_ROI_TC_10.csv TC_10"
[1] "TC_Cortical1"
[1] "tc 164_TC_ROI_TC_Cortical1.csv TC_Cortical1"
[1] "TC_Cortical2"
[1] "tc 164_TC_ROI_TC_Cortical2.csv TC_Cortical2"
[1] "TC_Cortical3"
[1] "tc 164_TC_ROI_TC_Cortical3.csv TC_Cortical3"
rownames(df_164) = 1:nrow(df_164)
jy_164 = df_164 %>%
dplyr::select(-c(area, IMAGE.NAME, X, Y)) %>%
t() %>%
CreateSeuratObject()
jy_164 <- NormalizeData(jy_164, scale.factor = 1e5) ###
Performing log-normalization
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
normed = GetAssayData(jy_164, slot = 'data')
normed[normed < 3] = 0
jy_164 <- SetAssayData(jy_164, slot = 'data', normed)
jy_164 <- FindVariableFeatures(jy_164, selection.method = "vst")
Calculating gene variances
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating feature variances of standardized and clipped values
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
all.genes <- rownames(jy_164)
jy_164 <- ScaleData(jy_164, features = all.genes)
Centering and scaling data matrix
|
| | 0%
|
|======================================================================================================================================================| 100%
jy_164 <- RunPCA(jy_164, approx = FALSE)
Warning: Requested number is larger than the number of available items (32). Setting to 32.
Warning: Requested number is larger than the number of available items (32). Setting to 32.
Warning: Requested number is larger than the number of available items (32). Setting to 32.
Warning: Requested number is larger than the number of available items (32). Setting to 32.
Warning: Requested number is larger than the number of available items (32). Setting to 32.
PC_ 1
Positive: NKX2.1, MAF1, SCGN, PROX1, GAD1, SP8, VIP, LHX6, GSX2, DCX
TSHZ1, CXCR7, COUPTF2, SST, DLX2, LRP8
Negative: KIA0319, SATB2, ASCL1, PAX6, CALB2, DCDC2, RELN, NCAM1, CXCL12, TBR1
EMX1, EGFR, CXCL14, VLDLR, CXCR4, EOMES
PC_ 2
Positive: DCX, SCGN, SP8, TBR1, EGFR, SATB2, DCDC2, EOMES, NKX2.1, LRP8
KIA0319, NCAM1, EMX1, CXCR7, TSHZ1, CALB2
Negative: GAD1, VIP, CXCR4, LHX6, MAF1, DLX2, SST, RELN, CXCL12, CXCL14
COUPTF2, PROX1, PAX6, GSX2, ASCL1, VLDLR
PC_ 3
Positive: COUPTF2, SP8, PROX1, SCGN, EOMES, TBR1, MAF1, CXCR7, NKX2.1, GSX2
TSHZ1, LHX6, DCDC2, EGFR, VIP, LRP8
Negative: SST, ASCL1, PAX6, RELN, CALB2, CXCL14, EMX1, CXCL12, DLX2, NCAM1
GAD1, CXCR4, KIA0319, DCX, VLDLR, SATB2
PC_ 4
Positive: TSHZ1, PROX1, SP8, ASCL1, DLX2, VLDLR, NKX2.1, NCAM1, PAX6, CXCR7
SCGN, EMX1, RELN, CXCL14, VIP, KIA0319
Negative: LRP8, TBR1, EOMES, CALB2, LHX6, GSX2, COUPTF2, DCX, MAF1, GAD1
CXCL12, DCDC2, SATB2, EGFR, CXCR4, SST
PC_ 5
Positive: PAX6, COUPTF2, RELN, TSHZ1, DLX2, SP8, CXCL12, SCGN, TBR1, GSX2
LRP8, DCX, SATB2, LHX6, ASCL1, GAD1
Negative: NKX2.1, DCDC2, EOMES, VLDLR, NCAM1, SST, CXCR7, EMX1, CXCR4, EGFR
MAF1, KIA0319, VIP, PROX1, CXCL14, CALB2
jy_164 <- FindNeighbors(jy_164, dims = 1:30)
Computing nearest neighbor graph
Computing SNN
jy_164 <- FindClusters(jy_164, resolution = 0.8)
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 802
Number of edges: 30100
Running Louvain algorithm...
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Maximum modularity in 10 random starts: 0.6519
Number of communities: 6
Elapsed time: 0 seconds
jy_164 <- RunUMAP(jy_164, dims = 1:30)
17:52:24 UMAP embedding parameters a = 0.9922 b = 1.112
17:52:24 Read 802 rows and found 30 numeric columns
17:52:24 Using Annoy for neighbor search, n_neighbors = 30
17:52:24 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
17:52:25 Writing NN index file to temp file /tmp/RtmpKuWa3v/filebb88138feb5f2
17:52:25 Searching Annoy index using 1 thread, search_k = 3000
17:52:25 Annoy recall = 100%
17:52:25 Commencing smooth kNN distance calibration using 1 thread
17:52:25 Initializing from normalized Laplacian + noise
17:52:25 Commencing optimization for 500 epochs, with 30056 positive edges
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
17:52:26 Optimization finished
DimPlot(jy_164, reduction = "umap", group.by = 'seurat_clusters') + NoAxes()
unique(df_164$IMAGE.NAME)
[1] "CC_8" "CC_10" "CC_Cortical1" "CC_Cortical2" "CC_L2-1" "CC_L2-2" "CC_L2-3" "CC_2" "CC_3" "CC_4"
[11] "CC_5" "CC_6" "CC_7" "CC_9" "TC_1" "TC_2" "TC_3" "TC_4" "TC_5" "TC_6"
[21] "TC_7" "TC_8" "TC_9" "TC_10" "TC_Cortical1" "TC_Cortical2" "TC_Cortical3"
images_ordered = c('TC_Cortical3', 'TC_Cortical2', 'TC_Cortical1', 'TC_10', 'TC_9', 'TC_8', 'TC_7', 'TC_6', 'TC_5', 'TC_4', 'TC_3', 'TC_2','TC_1','CC_2','CC_3',
'CC_4', 'CC_5', 'CC_6', 'CC_7', 'CC_8', 'CC_9', 'CC_10',
'CC_L2-1', 'CC_L2-2', 'CC_L2-3', 'CC_Cortical1', 'CC_Cortical2')
x_horz = 1:length(images_ordered) * 35
y_horz = rep(0, length(images_ordered))
horz_embedding = data.frame()
df_164$X_horz = -1
df_164$Y_horz = -1
images = list.files(meta_dir)
for(i in 1:length(images_ordered)){
image_name = images_ordered[i]
print(image_name)
split_names = strsplit(image_name, '_')
cortex = toupper(split_names[[1]][1])
number = split_names[[1]][2]
number_csv = paste0('_', number, '.csv')
filename = images[grepl(cortex, images) & grepl(number_csv, images) & grepl('164', images)]
coordinates = read.table(file.path(meta_dir, filename), sep = ',', header = TRUE)
if(image_name == "CC_L2-1"){
coordinates = coordinates[c(1:37, 39:nrow(coordinates)), ]
}
## checked already that lists are equal, missing 1, 18, 19 for now, layer 1 and others
## so this is a little tricky, so need to get it right
## Remember, it is the top right that the coordinate is coming from, but
## the bottom right is the new coordinate space.
## so first when we get the original coordinate space, to set to relative
## of bottom would be the same X, but 1024 - Y
## push out the coordinates for better visualization
#x_repelled <- (512 - coordinates$X_Coordinate_In_pixels)
df_164[df_164$IMAGE.NAME == image_name, 'X_horz'] = (coordinates$X_Coordinate_In_pixels /
IMAGE_SIZE * IMAGE_LEN) + y_horz[i]
df_164[df_164$IMAGE.NAME == image_name, 'Y_horz'] = ((1024-coordinates$Y_Coordinate_In_pixels) /
IMAGE_SIZE * IMAGE_LEN) + x_horz[i]
}
[1] "TC_Cortical3"
[1] "TC_Cortical2"
[1] "TC_Cortical1"
[1] "TC_10"
[1] "TC_9"
[1] "TC_8"
[1] "TC_7"
[1] "TC_6"
[1] "TC_5"
[1] "TC_4"
[1] "TC_3"
[1] "TC_2"
[1] "TC_1"
[1] "CC_2"
[1] "CC_3"
[1] "CC_4"
[1] "CC_5"
[1] "CC_6"
[1] "CC_7"
[1] "CC_8"
[1] "CC_9"
[1] "CC_10"
[1] "CC_L2-1"
[1] "CC_L2-2"
[1] "CC_L2-3"
[1] "CC_Cortical1"
[1] "CC_Cortical2"
hcoords = df_164 %>% dplyr::select(c('X_horz', 'Y_horz')) %>% as.matrix()
colnames(hcoords) <- c('pixel_1', 'pixel_2')
jy_164[["H"]] <- CreateDimReducObject(embeddings = hcoords, key = "pixel_", assay = DefaultAssay(jy_164))
jy_164<- RenameCells(jy_164, c(outer('164_', 1:ncol(jy_164), FUN=paste0)))
jy_164$area = df_164$area
jy_408<- RenameCells(jy_408, c(outer('408_', 1:ncol(jy_408), FUN=paste0)))
jy_408$area = df_408$area
jy_all <- merge(jy_164, jy_408)
jy_all <- NormalizeData(jy_all, scale.factor = 1e5) ###
Performing log-normalization
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
normed = GetAssayData(jy_all, slot = 'data')
normed[normed < 3] = 0
jy_all <- SetAssayData(jy_all, slot = 'data', normed)
jy_all <- FindVariableFeatures(jy_all, selection.method = "vst")
Calculating gene variances
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating feature variances of standardized and clipped values
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
all.genes <- rownames(jy_all)
jy_all <- ScaleData(jy_all, features = all.genes)
Centering and scaling data matrix
|
| | 0%
|
|======================================================================================================================================================| 100%
jy_all <- RunPCA(jy_all, approx = FALSE)
Warning: Requested number is larger than the number of available items (32). Setting to 32.
Warning: Requested number is larger than the number of available items (32). Setting to 32.
Warning: Requested number is larger than the number of available items (32). Setting to 32.
Warning: Requested number is larger than the number of available items (32). Setting to 32.
Warning: Requested number is larger than the number of available items (32). Setting to 32.
PC_ 1
Positive: ASCL1, CXCR4, KIA0319, SATB2, RELN, PAX6, CXCL12, EMX1, VIP, CALB2
DCDC2, DLX2, VLDLR, EOMES, SST, LHX6
Negative: DCX, SCGN, GSX2, NKX2.1, TSHZ1, TBR1, LRP8, COUPTF2, SP8, EGFR
CXCR7, CXCL14, PROX1, MAF1, GAD1, NCAM1
PC_ 2
Positive: GAD1, MAF1, VIP, LHX6, NKX2.1, PROX1, DLX2, CXCR4, SST, TSHZ1
SP8, GSX2, VLDLR, RELN, CXCR7, COUPTF2
Negative: TBR1, DCX, KIA0319, DCDC2, LRP8, EOMES, SATB2, EGFR, CALB2, CXCL12
NCAM1, EMX1, PAX6, ASCL1, CXCL14, SCGN
PC_ 3
Positive: NCAM1, RELN, ASCL1, PAX6, SATB2, SST, MAF1, EMX1, VLDLR, EGFR
CXCR7, CXCL14, TSHZ1, DCX, SCGN, GSX2
Negative: COUPTF2, LRP8, LHX6, EOMES, TBR1, SP8, GAD1, DLX2, CXCR4, PROX1
NKX2.1, CALB2, CXCL12, DCDC2, VIP, KIA0319
PC_ 4
Positive: SST, RELN, DLX2, LRP8, LHX6, PAX6, GAD1, CALB2, CXCL12, CXCR4
TBR1, VIP, ASCL1, SATB2, KIA0319, EMX1
Negative: TSHZ1, PROX1, CXCR7, NCAM1, SCGN, NKX2.1, EGFR, VLDLR, SP8, CXCL14
GSX2, DCX, DCDC2, COUPTF2, EOMES, MAF1
PC_ 5
Positive: SP8, SCGN, PAX6, DLX2, EMX1, NKX2.1, SATB2, COUPTF2, ASCL1, SST
RELN, PROX1, VLDLR, TBR1, DCX, KIA0319
Negative: GAD1, CXCL14, NCAM1, EGFR, VIP, LRP8, DCDC2, CXCR4, MAF1, CXCR7
GSX2, LHX6, CXCL12, EOMES, CALB2, TSHZ1
jy_all <- FindNeighbors(jy_all, dims = 1:30)
Computing nearest neighbor graph
Computing SNN
jy_all <- FindClusters(jy_all, resolution = 1.5)
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 1815
Number of edges: 61694
Running Louvain algorithm...
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Maximum modularity in 10 random starts: 0.6181
Number of communities: 13
Elapsed time: 0 seconds
jy_all <- RunUMAP(jy_all, dims = 1:30)
17:52:28 UMAP embedding parameters a = 0.9922 b = 1.112
17:52:28 Read 1815 rows and found 30 numeric columns
17:52:28 Using Annoy for neighbor search, n_neighbors = 30
17:52:28 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
17:52:28 Writing NN index file to temp file /tmp/RtmpKuWa3v/filebb88165be5c09
17:52:28 Searching Annoy index using 1 thread, search_k = 3000
17:52:29 Annoy recall = 100%
17:52:29 Commencing smooth kNN distance calibration using 1 thread
17:52:30 Initializing from normalized Laplacian + noise
17:52:30 Commencing optimization for 500 epochs, with 70074 positive edges
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
17:52:32 Optimization finished
DimPlot(jy_all, reduction = "umap", group.by = 'seurat_clusters') + NoAxes()
big_dimplot <- function(sobj, grep_pattern){
dp = DimPlot(sobj, cells.highlight = list(imp = which(grepl(grep_pattern, sobj$area))))
dp <- dp + scale_color_manual(values = c('grey90', '#1982c4'), labels=c('other', grep_pattern)) + NoAxes()
}
patterns = c('408_CC', '408_MS_CC', 'vMS_TC', 'dMS_TC', '408_TC', '164_CC', '164_MS_CC', '164_MS_TC', '164_TC')
plots <- lapply(1:length(patterns), function(i){
big_dimplot(jy_all, grep_pattern = patterns[i])
})
Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.
Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.
Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.
Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.
Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.
Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.
Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.
Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.
Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.
areas = plot_grid(plotlist = plots, label_size = 10, nrow = 3)
areas
ggsave(plot = areas, filename = 'test_all_area_plots_size1_alpha1.png', path = file.path(output_dir_plot, '20220721_1'), width = 14, height = 8, dpi = 150)
plot_clusters_umap(jy_all, clustering ='RNA_snn_res.1.5', pt.size = 2.0)
Error in plot_clusters_umap(jy_all, clustering = "RNA_snn_res.1.5", pt.size = 2) :
could not find function "plot_clusters_umap"
jy_all.markers <- FindAllMarkers(jy_all, only.pos = TRUE, min.pct = 0.25, logfc.threshold = 0.25)
Calculating cluster 0
| | 0 % ~calculating
|++++ | 8 % ~00s
|++++++++ | 15% ~00s
|++++++++++++ | 23% ~00s
|++++++++++++++++ | 31% ~00s
|++++++++++++++++++++ | 38% ~00s
|++++++++++++++++++++++++ | 46% ~00s
|+++++++++++++++++++++++++++ | 54% ~00s
|+++++++++++++++++++++++++++++++ | 62% ~00s
|+++++++++++++++++++++++++++++++++++ | 69% ~00s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~00s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 1
| | 0 % ~calculating
|+++++ | 9 % ~00s
|++++++++++ | 18% ~00s
|++++++++++++++ | 27% ~00s
|+++++++++++++++++++ | 36% ~00s
|+++++++++++++++++++++++ | 45% ~00s
|++++++++++++++++++++++++++++ | 55% ~00s
|++++++++++++++++++++++++++++++++ | 64% ~00s
|+++++++++++++++++++++++++++++++++++++ | 73% ~00s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 2
| | 0 % ~calculating
|++++++++++ | 20% ~00s
|++++++++++++++++++++ | 40% ~00s
|++++++++++++++++++++++++++++++ | 60% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 3
| | 0 % ~calculating
|++++++++ | 14% ~00s
|+++++++++++++++ | 29% ~00s
|++++++++++++++++++++++ | 43% ~00s
|+++++++++++++++++++++++++++++ | 57% ~00s
|++++++++++++++++++++++++++++++++++++ | 71% ~00s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 4
| | 0 % ~calculating
|+++++++++ | 17% ~00s
|+++++++++++++++++ | 33% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++++++ | 67% ~00s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 5
| | 0 % ~calculating
|+++++++ | 12% ~00s
|+++++++++++++ | 25% ~00s
|+++++++++++++++++++ | 38% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++++ | 62% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 6
| | 0 % ~calculating
|++++ | 8 % ~00s
|++++++++ | 15% ~00s
|++++++++++++ | 23% ~00s
|++++++++++++++++ | 31% ~00s
|++++++++++++++++++++ | 38% ~00s
|++++++++++++++++++++++++ | 46% ~00s
|+++++++++++++++++++++++++++ | 54% ~00s
|+++++++++++++++++++++++++++++++ | 62% ~00s
|+++++++++++++++++++++++++++++++++++ | 69% ~00s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~00s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
jy_all.markers %>%
group_by(cluster) %>%
slice_max(n = 32, order_by = avg_log2FC)
clusters_164 = jy_all$RNA_snn_res.1.5[1:ncol(jy_164)]
jy_164$unified_clusters = clusters_164
clusters_408 = jy_all$RNA_snn_res.1.5[(ncol(jy_164)+1):ncol(jy_all)]
jy_408$unified_clusters = clusters_408
clusters = as.numeric(sort(unique(jy_408$unified_clusters)))
plots <- lapply(1:length(clusters), function(i){
plot_clusters_vertical_spatial(jy_408, cluster = clusters[i], pt.size = 1, clustering = 'unified_clusters', anterior = FALSE)
})
verts= plot_grid(plotlist = plots, label_size = 10, nrow = 1)
ggsave(plot = verts, filename = 'test_408_unified_vertical_cluster_plots_size1_alpha1.png', path = file.path(output_dir_plot, '20220721_1'), width = 5, height = 8, dpi = 150)
clusters = as.numeric(sort(unique(jy_164$unified_clusters)))
plots <- lapply(1:length(clusters), function(i){
plot_clusters_vertical_spatial(jy_164, cluster = clusters[i], pt.size = 1, clustering = 'unified_clusters', anterior = TRUE)
})
verts= plot_grid(plotlist = plots, label_size = 10, nrow = 1)
ggsave(plot = verts, filename = 'test_164_unified_vertical_cluster_plots_size1_alpha1.png', path = file.path(output_dir_plot, '20220721_1'), width = 5, height = 8, dpi = 150)
genes = rownames(markers2)
plots <- lapply(1:length(genes), function(i){
plot_features_umap(jy_all, genes[i], pt.size = 0.5)
})
umaps = plot_grid(plotlist = plots, label_size = 10, nrow = 1)
umaps
ggsave(plot = umaps, filename = 'cluster7_markers_all_umapl_expr_plots_size1_alpha1.png', path = file.path(output_dir_plot, '20220721_1'), width = 8, height = 1, dpi = 150)
jy_all_7.markers <- FindMarkers(jy_all, ident.1 = 7, ident.2 = NULL, only.pos = FALSE)
markers2 = jy_all_7.markers %>%
slice_max(n = 5, order_by = avg_log2FC)
markers2
new.cluster.ids = c('TBR1+/LRP8+ MS to TC',
'PROX1 and NKX2.1 Immature INs',
'Immature CALB2+ CGE IN',
'Excitatory CXCL12+ Neurons',
'GSX2- dLGE INs',
'Mature, SST+ MGE INs',
'Mature, VIP+ CGE INs',
'MAF1+/TSHZ1+ Immature INs',
'Layer 2 Excitatory Neurons',
'CXCR4+ Posterior, MS to TC INs',
'EMX1+/NKX2.1+ Anterior, MS to CC INs',
'EMX1+/LHX6+ Posterior, MS to CC INs',
'GSX2+/RELN+/PAX6+ dLGE INs')
names(new.cluster.ids) <- levels(jy_all)
jy_all <- RenameIdents(jy_all, new.cluster.ids)
library(ggrepel)
plot_clusters_umap <- function(sobj, clustering, pt.size = 3, space = "umap")
{
coordinates <- Embeddings(sobj, reduction = space)
#expmat <- sobj[[clustering]]
expmat = as.character(Idents(jy_all))
gene_df <- as.data.frame(cbind(coordinates, expmat))
colnames(gene_df) <- c('X', 'Y', 'expr')
gene_df$X = as.numeric(gene_df$X)
gene_df$Y = as.numeric(gene_df$Y)
summary_gene_df = gene_df %>% dplyr::group_by(expr) %>% dplyr::summarise(xmean = mean(X), ymean = mean(Y))
plot <- ggplot(gene_df, aes(x = X, y = Y, color = as.factor(expr))) + geom_point(size = pt.size, alpha = 0.8) + #+ geom_label_repel(data = summary_gene_df,
# mapping = aes(x = xmean,
#y = ymean),
#label = summary_gene_df$expr) +
theme_classic() + ggtitle(clustering) + NoAxes() + #NoLegend() +
theme(title = element_text(face = 'bold', size = rel(1), hjust = 1))
cluster_colors = scales::hue_pal()(length(unique(expmat)))
plot = plot + scale_colour_manual(values = cluster_colors)
return(plot)
}
plot_clusters_umap(jy_all, clustering = '', pt.size = 2.0)
immature_IN_names = c('PROX1 and NKX2.1 Immature INs',
'Immature CALB2+ CGE interneuron',
'MAF1+/TSHZ1+ Immature INs',
'CXCR4+ Posterior, MS to TC INs',
'EMX1+/NKX2.1+ Anterior, MS to CC INs',
'EMX1+/LHX6+ Posterior, MS to CC INs',
'GSX2+/RELN+/PAX6+ Clump')
jy_immature <- jy_all[, Idents(jy_all) %in% immature_IN_names]
breakpoints = 1:20/10+0.3
plots = list()
jy_immature <- FindNeighbors(jy_immature, dims = 1:30)
Computing nearest neighbor graph
Computing SNN
i = 1
for (breakpoint in breakpoints){
jy_immature <- FindClusters(jy_immature, resolution = breakpoint)
jy_immature <- RunUMAP(jy_immature, dims = 1:30)
jy_immature.markers <- FindAllMarkers(jy_immature, only.pos = TRUE, min.pct = 0.25, logfc.threshold = 0.25)
labels = jy_immature.markers %>%
group_by(cluster) %>%
slice_max(n = 1, order_by = avg_log2FC)
new.cluster.ids <- labels$gene
names(new.cluster.ids) <- levels(jy_immature)
jy_immature <- RenameIdents(jy_immature, new.cluster.ids)
plots[[i]] = DimPlot(jy_immature, reduction = "umap", pt.size = 1, label = TRUE) + NoAxes() + NoLegend() + ggtitle(breakpoint)
i = i + 1
}
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 786
Number of edges: 29724
Running Louvain algorithm...
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Maximum modularity in 10 random starts: 0.7886
Number of communities: 5
Elapsed time: 0 seconds
00:33:01 UMAP embedding parameters a = 0.9922 b = 1.112
00:33:01 Read 786 rows and found 30 numeric columns
00:33:01 Using Annoy for neighbor search, n_neighbors = 30
00:33:01 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:33:01 Writing NN index file to temp file /tmp/RtmpIqRk2o/file74f044020edf1
00:33:01 Searching Annoy index using 1 thread, search_k = 3000
00:33:01 Annoy recall = 100%
00:33:01 Commencing smooth kNN distance calibration using 1 thread
00:33:02 Initializing from normalized Laplacian + noise
00:33:02 Commencing optimization for 500 epochs, with 29334 positive edges
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:33:03 Optimization finished
Calculating cluster 0
| | 0 % ~calculating
|++++ | 7 % ~00s
|+++++++ | 13% ~00s
|++++++++++ | 20% ~00s
|++++++++++++++ | 27% ~00s
|+++++++++++++++++ | 33% ~00s
|++++++++++++++++++++ | 40% ~00s
|++++++++++++++++++++++++ | 47% ~00s
|+++++++++++++++++++++++++++ | 53% ~00s
|++++++++++++++++++++++++++++++ | 60% ~00s
|++++++++++++++++++++++++++++++++++ | 67% ~00s
|+++++++++++++++++++++++++++++++++++++ | 73% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 1
| | 0 % ~calculating
|+++++ | 9 % ~00s
|++++++++++ | 18% ~00s
|++++++++++++++ | 27% ~00s
|+++++++++++++++++++ | 36% ~00s
|+++++++++++++++++++++++ | 45% ~00s
|++++++++++++++++++++++++++++ | 55% ~00s
|++++++++++++++++++++++++++++++++ | 64% ~00s
|+++++++++++++++++++++++++++++++++++++ | 73% ~00s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 2
| | 0 % ~calculating
|+++++ | 8 % ~00s
|+++++++++ | 17% ~00s
|+++++++++++++ | 25% ~00s
|+++++++++++++++++ | 33% ~00s
|+++++++++++++++++++++ | 42% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++ | 58% ~00s
|++++++++++++++++++++++++++++++++++ | 67% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 3
| | 0 % ~calculating
|+++++ | 8 % ~00s
|+++++++++ | 17% ~00s
|+++++++++++++ | 25% ~00s
|+++++++++++++++++ | 33% ~00s
|+++++++++++++++++++++ | 42% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++ | 58% ~00s
|++++++++++++++++++++++++++++++++++ | 67% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 4
| | 0 % ~calculating
|+++++++ | 12% ~00s
|+++++++++++++ | 25% ~00s
|+++++++++++++++++++ | 38% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++++ | 62% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 786
Number of edges: 29724
Running Louvain algorithm...
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Maximum modularity in 10 random starts: 0.7560
Number of communities: 6
Elapsed time: 0 seconds
00:33:03 UMAP embedding parameters a = 0.9922 b = 1.112
00:33:03 Read 786 rows and found 30 numeric columns
00:33:03 Using Annoy for neighbor search, n_neighbors = 30
00:33:03 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:33:04 Writing NN index file to temp file /tmp/RtmpIqRk2o/file74f0427cd431a
00:33:04 Searching Annoy index using 1 thread, search_k = 3000
00:33:04 Annoy recall = 100%
00:33:04 Commencing smooth kNN distance calibration using 1 thread
00:33:05 Initializing from normalized Laplacian + noise
00:33:05 Commencing optimization for 500 epochs, with 29334 positive edges
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:33:06 Optimization finished
Calculating cluster 0
| | 0 % ~calculating
|++++++++ | 14% ~00s
|+++++++++++++++ | 29% ~00s
|++++++++++++++++++++++ | 43% ~00s
|+++++++++++++++++++++++++++++ | 57% ~00s
|++++++++++++++++++++++++++++++++++++ | 71% ~00s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 1
| | 0 % ~calculating
|+++++ | 9 % ~00s
|++++++++++ | 18% ~00s
|++++++++++++++ | 27% ~00s
|+++++++++++++++++++ | 36% ~00s
|+++++++++++++++++++++++ | 45% ~00s
|++++++++++++++++++++++++++++ | 55% ~00s
|++++++++++++++++++++++++++++++++ | 64% ~00s
|+++++++++++++++++++++++++++++++++++++ | 73% ~00s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 2
| | 0 % ~calculating
|++++ | 7 % ~00s
|++++++++ | 14% ~00s
|+++++++++++ | 21% ~00s
|+++++++++++++++ | 29% ~00s
|++++++++++++++++++ | 36% ~00s
|++++++++++++++++++++++ | 43% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|+++++++++++++++++++++++++++++ | 57% ~00s
|+++++++++++++++++++++++++++++++++ | 64% ~00s
|++++++++++++++++++++++++++++++++++++ | 71% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~00s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 3
| | 0 % ~calculating
|+++++ | 8 % ~00s
|+++++++++ | 17% ~00s
|+++++++++++++ | 25% ~00s
|+++++++++++++++++ | 33% ~00s
|+++++++++++++++++++++ | 42% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++ | 58% ~00s
|++++++++++++++++++++++++++++++++++ | 67% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 4
| | 0 % ~calculating
|+++++ | 8 % ~00s
|+++++++++ | 17% ~00s
|+++++++++++++ | 25% ~00s
|+++++++++++++++++ | 33% ~00s
|+++++++++++++++++++++ | 42% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++ | 58% ~00s
|++++++++++++++++++++++++++++++++++ | 67% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 5
| | 0 % ~calculating
|+++++++ | 12% ~00s
|+++++++++++++ | 25% ~00s
|+++++++++++++++++++ | 38% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++++ | 62% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 786
Number of edges: 29724
Running Louvain algorithm...
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Maximum modularity in 10 random starts: 0.7291
Number of communities: 6
Elapsed time: 0 seconds
00:33:06 UMAP embedding parameters a = 0.9922 b = 1.112
00:33:06 Read 786 rows and found 30 numeric columns
00:33:06 Using Annoy for neighbor search, n_neighbors = 30
00:33:06 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:33:06 Writing NN index file to temp file /tmp/RtmpIqRk2o/file74f045dbf24c
00:33:06 Searching Annoy index using 1 thread, search_k = 3000
00:33:07 Annoy recall = 100%
00:33:07 Commencing smooth kNN distance calibration using 1 thread
00:33:08 Initializing from normalized Laplacian + noise
00:33:08 Commencing optimization for 500 epochs, with 29334 positive edges
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:33:09 Optimization finished
Calculating cluster 0
| | 0 % ~calculating
|++++++++ | 14% ~00s
|+++++++++++++++ | 29% ~00s
|++++++++++++++++++++++ | 43% ~00s
|+++++++++++++++++++++++++++++ | 57% ~00s
|++++++++++++++++++++++++++++++++++++ | 71% ~00s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 1
| | 0 % ~calculating
|+++++ | 9 % ~00s
|++++++++++ | 18% ~00s
|++++++++++++++ | 27% ~00s
|+++++++++++++++++++ | 36% ~00s
|+++++++++++++++++++++++ | 45% ~00s
|++++++++++++++++++++++++++++ | 55% ~00s
|++++++++++++++++++++++++++++++++ | 64% ~00s
|+++++++++++++++++++++++++++++++++++++ | 73% ~00s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 2
| | 0 % ~calculating
|++++ | 7 % ~00s
|++++++++ | 14% ~00s
|+++++++++++ | 21% ~00s
|+++++++++++++++ | 29% ~00s
|++++++++++++++++++ | 36% ~00s
|++++++++++++++++++++++ | 43% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|+++++++++++++++++++++++++++++ | 57% ~00s
|+++++++++++++++++++++++++++++++++ | 64% ~00s
|++++++++++++++++++++++++++++++++++++ | 71% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~00s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 3
| | 0 % ~calculating
|+++++ | 8 % ~00s
|+++++++++ | 17% ~00s
|+++++++++++++ | 25% ~00s
|+++++++++++++++++ | 33% ~00s
|+++++++++++++++++++++ | 42% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++ | 58% ~00s
|++++++++++++++++++++++++++++++++++ | 67% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 4
| | 0 % ~calculating
|+++++ | 8 % ~00s
|+++++++++ | 17% ~00s
|+++++++++++++ | 25% ~00s
|+++++++++++++++++ | 33% ~00s
|+++++++++++++++++++++ | 42% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++ | 58% ~00s
|++++++++++++++++++++++++++++++++++ | 67% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 5
| | 0 % ~calculating
|+++++++ | 12% ~00s
|+++++++++++++ | 25% ~00s
|+++++++++++++++++++ | 38% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++++ | 62% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 786
Number of edges: 29724
Running Louvain algorithm...
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Maximum modularity in 10 random starts: 0.7022
Number of communities: 6
Elapsed time: 0 seconds
00:33:09 UMAP embedding parameters a = 0.9922 b = 1.112
00:33:09 Read 786 rows and found 30 numeric columns
00:33:09 Using Annoy for neighbor search, n_neighbors = 30
00:33:09 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:33:09 Writing NN index file to temp file /tmp/RtmpIqRk2o/file74f043bd0dbd8
00:33:09 Searching Annoy index using 1 thread, search_k = 3000
00:33:09 Annoy recall = 100%
00:33:10 Commencing smooth kNN distance calibration using 1 thread
00:33:10 Initializing from normalized Laplacian + noise
00:33:10 Commencing optimization for 500 epochs, with 29334 positive edges
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:33:12 Optimization finished
Calculating cluster 0
| | 0 % ~calculating
|++++++++ | 14% ~00s
|+++++++++++++++ | 29% ~00s
|++++++++++++++++++++++ | 43% ~00s
|+++++++++++++++++++++++++++++ | 57% ~00s
|++++++++++++++++++++++++++++++++++++ | 71% ~00s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 1
| | 0 % ~calculating
|+++++ | 9 % ~00s
|++++++++++ | 18% ~00s
|++++++++++++++ | 27% ~00s
|+++++++++++++++++++ | 36% ~00s
|+++++++++++++++++++++++ | 45% ~00s
|++++++++++++++++++++++++++++ | 55% ~00s
|++++++++++++++++++++++++++++++++ | 64% ~00s
|+++++++++++++++++++++++++++++++++++++ | 73% ~00s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 2
| | 0 % ~calculating
|++++ | 7 % ~00s
|++++++++ | 14% ~00s
|+++++++++++ | 21% ~00s
|+++++++++++++++ | 29% ~00s
|++++++++++++++++++ | 36% ~00s
|++++++++++++++++++++++ | 43% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|+++++++++++++++++++++++++++++ | 57% ~00s
|+++++++++++++++++++++++++++++++++ | 64% ~00s
|++++++++++++++++++++++++++++++++++++ | 71% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~00s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 3
| | 0 % ~calculating
|+++++ | 8 % ~00s
|+++++++++ | 17% ~00s
|+++++++++++++ | 25% ~00s
|+++++++++++++++++ | 33% ~00s
|+++++++++++++++++++++ | 42% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++ | 58% ~00s
|++++++++++++++++++++++++++++++++++ | 67% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 4
| | 0 % ~calculating
|+++++ | 8 % ~00s
|+++++++++ | 17% ~00s
|+++++++++++++ | 25% ~00s
|+++++++++++++++++ | 33% ~00s
|+++++++++++++++++++++ | 42% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++ | 58% ~00s
|++++++++++++++++++++++++++++++++++ | 67% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 5
| | 0 % ~calculating
|+++++++ | 12% ~00s
|+++++++++++++ | 25% ~00s
|+++++++++++++++++++ | 38% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++++ | 62% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 786
Number of edges: 29724
Running Louvain algorithm...
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Maximum modularity in 10 random starts: 0.6773
Number of communities: 6
Elapsed time: 0 seconds
00:33:12 UMAP embedding parameters a = 0.9922 b = 1.112
00:33:12 Read 786 rows and found 30 numeric columns
00:33:12 Using Annoy for neighbor search, n_neighbors = 30
00:33:12 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:33:12 Writing NN index file to temp file /tmp/RtmpIqRk2o/file74f046b090e3b
00:33:12 Searching Annoy index using 1 thread, search_k = 3000
00:33:12 Annoy recall = 100%
00:33:13 Commencing smooth kNN distance calibration using 1 thread
00:33:13 Initializing from normalized Laplacian + noise
00:33:13 Commencing optimization for 500 epochs, with 29334 positive edges
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:33:14 Optimization finished
Calculating cluster 0
| | 0 % ~calculating
|++++++++ | 14% ~00s
|+++++++++++++++ | 29% ~00s
|++++++++++++++++++++++ | 43% ~00s
|+++++++++++++++++++++++++++++ | 57% ~00s
|++++++++++++++++++++++++++++++++++++ | 71% ~00s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 1
| | 0 % ~calculating
|+++++ | 9 % ~00s
|++++++++++ | 18% ~00s
|++++++++++++++ | 27% ~00s
|+++++++++++++++++++ | 36% ~00s
|+++++++++++++++++++++++ | 45% ~00s
|++++++++++++++++++++++++++++ | 55% ~00s
|++++++++++++++++++++++++++++++++ | 64% ~00s
|+++++++++++++++++++++++++++++++++++++ | 73% ~00s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 2
| | 0 % ~calculating
|++++ | 7 % ~00s
|++++++++ | 14% ~00s
|+++++++++++ | 21% ~00s
|+++++++++++++++ | 29% ~00s
|++++++++++++++++++ | 36% ~00s
|++++++++++++++++++++++ | 43% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|+++++++++++++++++++++++++++++ | 57% ~00s
|+++++++++++++++++++++++++++++++++ | 64% ~00s
|++++++++++++++++++++++++++++++++++++ | 71% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~00s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 3
| | 0 % ~calculating
|+++++ | 8 % ~00s
|+++++++++ | 17% ~00s
|+++++++++++++ | 25% ~00s
|+++++++++++++++++ | 33% ~00s
|+++++++++++++++++++++ | 42% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++ | 58% ~00s
|++++++++++++++++++++++++++++++++++ | 67% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 4
| | 0 % ~calculating
|+++++ | 8 % ~00s
|+++++++++ | 17% ~00s
|+++++++++++++ | 25% ~00s
|+++++++++++++++++ | 33% ~00s
|+++++++++++++++++++++ | 42% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++ | 58% ~00s
|++++++++++++++++++++++++++++++++++ | 67% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 5
| | 0 % ~calculating
|+++++ | 8 % ~00s
|+++++++++ | 17% ~00s
|+++++++++++++ | 25% ~00s
|+++++++++++++++++ | 33% ~00s
|+++++++++++++++++++++ | 42% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++ | 58% ~00s
|++++++++++++++++++++++++++++++++++ | 67% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 786
Number of edges: 29724
Running Louvain algorithm...
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Maximum modularity in 10 random starts: 0.6532
Number of communities: 6
Elapsed time: 0 seconds
00:33:15 UMAP embedding parameters a = 0.9922 b = 1.112
00:33:15 Read 786 rows and found 30 numeric columns
00:33:15 Using Annoy for neighbor search, n_neighbors = 30
00:33:15 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:33:15 Writing NN index file to temp file /tmp/RtmpIqRk2o/file74f042c056235
00:33:15 Searching Annoy index using 1 thread, search_k = 3000
00:33:15 Annoy recall = 100%
00:33:16 Commencing smooth kNN distance calibration using 1 thread
00:33:16 Initializing from normalized Laplacian + noise
00:33:16 Commencing optimization for 500 epochs, with 29334 positive edges
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:33:17 Optimization finished
Calculating cluster 0
| | 0 % ~calculating
|++++++++ | 14% ~00s
|+++++++++++++++ | 29% ~00s
|++++++++++++++++++++++ | 43% ~00s
|+++++++++++++++++++++++++++++ | 57% ~00s
|++++++++++++++++++++++++++++++++++++ | 71% ~00s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 1
| | 0 % ~calculating
|+++++ | 9 % ~00s
|++++++++++ | 18% ~00s
|++++++++++++++ | 27% ~00s
|+++++++++++++++++++ | 36% ~00s
|+++++++++++++++++++++++ | 45% ~00s
|++++++++++++++++++++++++++++ | 55% ~00s
|++++++++++++++++++++++++++++++++ | 64% ~00s
|+++++++++++++++++++++++++++++++++++++ | 73% ~00s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 2
| | 0 % ~calculating
|++++ | 7 % ~00s
|++++++++ | 14% ~00s
|+++++++++++ | 21% ~00s
|+++++++++++++++ | 29% ~00s
|++++++++++++++++++ | 36% ~00s
|++++++++++++++++++++++ | 43% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|+++++++++++++++++++++++++++++ | 57% ~00s
|+++++++++++++++++++++++++++++++++ | 64% ~00s
|++++++++++++++++++++++++++++++++++++ | 71% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~00s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 3
| | 0 % ~calculating
|+++++ | 8 % ~00s
|+++++++++ | 17% ~00s
|+++++++++++++ | 25% ~00s
|+++++++++++++++++ | 33% ~00s
|+++++++++++++++++++++ | 42% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++ | 58% ~00s
|++++++++++++++++++++++++++++++++++ | 67% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 4
| | 0 % ~calculating
|+++++ | 8 % ~00s
|+++++++++ | 17% ~00s
|+++++++++++++ | 25% ~00s
|+++++++++++++++++ | 33% ~00s
|+++++++++++++++++++++ | 42% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++ | 58% ~00s
|++++++++++++++++++++++++++++++++++ | 67% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 5
| | 0 % ~calculating
|+++++ | 8 % ~00s
|+++++++++ | 17% ~00s
|+++++++++++++ | 25% ~00s
|+++++++++++++++++ | 33% ~00s
|+++++++++++++++++++++ | 42% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++ | 58% ~00s
|++++++++++++++++++++++++++++++++++ | 67% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 786
Number of edges: 29724
Running Louvain algorithm...
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Maximum modularity in 10 random starts: 0.6298
Number of communities: 7
Elapsed time: 0 seconds
00:33:18 UMAP embedding parameters a = 0.9922 b = 1.112
00:33:18 Read 786 rows and found 30 numeric columns
00:33:18 Using Annoy for neighbor search, n_neighbors = 30
00:33:18 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:33:18 Writing NN index file to temp file /tmp/RtmpIqRk2o/file74f0447441d4f
00:33:18 Searching Annoy index using 1 thread, search_k = 3000
00:33:18 Annoy recall = 100%
00:33:18 Commencing smooth kNN distance calibration using 1 thread
00:33:19 Initializing from normalized Laplacian + noise
00:33:19 Commencing optimization for 500 epochs, with 29334 positive edges
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:33:20 Optimization finished
Calculating cluster 0
| | 0 % ~calculating
|++++++++ | 14% ~00s
|+++++++++++++++ | 29% ~00s
|++++++++++++++++++++++ | 43% ~00s
|+++++++++++++++++++++++++++++ | 57% ~00s
|++++++++++++++++++++++++++++++++++++ | 71% ~00s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 1
| | 0 % ~calculating
|+++++ | 9 % ~00s
|++++++++++ | 18% ~00s
|++++++++++++++ | 27% ~00s
|+++++++++++++++++++ | 36% ~00s
|+++++++++++++++++++++++ | 45% ~00s
|++++++++++++++++++++++++++++ | 55% ~00s
|++++++++++++++++++++++++++++++++ | 64% ~00s
|+++++++++++++++++++++++++++++++++++++ | 73% ~00s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 2
| | 0 % ~calculating
|++++ | 7 % ~00s
|++++++++ | 14% ~00s
|+++++++++++ | 21% ~00s
|+++++++++++++++ | 29% ~00s
|++++++++++++++++++ | 36% ~00s
|++++++++++++++++++++++ | 43% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|+++++++++++++++++++++++++++++ | 57% ~00s
|+++++++++++++++++++++++++++++++++ | 64% ~00s
|++++++++++++++++++++++++++++++++++++ | 71% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~00s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 3
| | 0 % ~calculating
|+++++ | 8 % ~00s
|+++++++++ | 17% ~00s
|+++++++++++++ | 25% ~00s
|+++++++++++++++++ | 33% ~00s
|+++++++++++++++++++++ | 42% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++ | 58% ~00s
|++++++++++++++++++++++++++++++++++ | 67% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 4
| | 0 % ~calculating
|+++++ | 10% ~00s
|++++++++++ | 20% ~00s
|+++++++++++++++ | 30% ~00s
|++++++++++++++++++++ | 40% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++ | 60% ~00s
|+++++++++++++++++++++++++++++++++++ | 70% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 5
| | 0 % ~calculating
|+++++ | 8 % ~00s
|+++++++++ | 17% ~00s
|+++++++++++++ | 25% ~00s
|+++++++++++++++++ | 33% ~00s
|+++++++++++++++++++++ | 42% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++ | 58% ~00s
|++++++++++++++++++++++++++++++++++ | 67% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 6
| | 0 % ~calculating
|+++++++ | 12% ~00s
|+++++++++++++ | 25% ~00s
|+++++++++++++++++++ | 38% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++++ | 62% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 786
Number of edges: 29724
Running Louvain algorithm...
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Maximum modularity in 10 random starts: 0.6063
Number of communities: 7
Elapsed time: 0 seconds
00:33:21 UMAP embedding parameters a = 0.9922 b = 1.112
00:33:21 Read 786 rows and found 30 numeric columns
00:33:21 Using Annoy for neighbor search, n_neighbors = 30
00:33:21 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:33:21 Writing NN index file to temp file /tmp/RtmpIqRk2o/file74f0433a47f83
00:33:21 Searching Annoy index using 1 thread, search_k = 3000
00:33:21 Annoy recall = 100%
00:33:21 Commencing smooth kNN distance calibration using 1 thread
00:33:22 Initializing from normalized Laplacian + noise
00:33:22 Commencing optimization for 500 epochs, with 29334 positive edges
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:33:23 Optimization finished
Calculating cluster 0
| | 0 % ~calculating
|+++++ | 9 % ~00s
|++++++++++ | 18% ~00s
|++++++++++++++ | 27% ~00s
|+++++++++++++++++++ | 36% ~00s
|+++++++++++++++++++++++ | 45% ~00s
|++++++++++++++++++++++++++++ | 55% ~00s
|++++++++++++++++++++++++++++++++ | 64% ~00s
|+++++++++++++++++++++++++++++++++++++ | 73% ~00s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 1
| | 0 % ~calculating
|+++++++ | 12% ~00s
|+++++++++++++ | 25% ~00s
|+++++++++++++++++++ | 38% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++++ | 62% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 2
| | 0 % ~calculating
|++++ | 7 % ~00s
|++++++++ | 14% ~00s
|+++++++++++ | 21% ~00s
|+++++++++++++++ | 29% ~00s
|++++++++++++++++++ | 36% ~00s
|++++++++++++++++++++++ | 43% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|+++++++++++++++++++++++++++++ | 57% ~00s
|+++++++++++++++++++++++++++++++++ | 64% ~00s
|++++++++++++++++++++++++++++++++++++ | 71% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~00s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 3
| | 0 % ~calculating
|+++++ | 8 % ~00s
|+++++++++ | 17% ~00s
|+++++++++++++ | 25% ~00s
|+++++++++++++++++ | 33% ~00s
|+++++++++++++++++++++ | 42% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++ | 58% ~00s
|++++++++++++++++++++++++++++++++++ | 67% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 4
| | 0 % ~calculating
|+++++ | 9 % ~00s
|++++++++++ | 18% ~00s
|++++++++++++++ | 27% ~00s
|+++++++++++++++++++ | 36% ~00s
|+++++++++++++++++++++++ | 45% ~00s
|++++++++++++++++++++++++++++ | 55% ~00s
|++++++++++++++++++++++++++++++++ | 64% ~00s
|+++++++++++++++++++++++++++++++++++++ | 73% ~00s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 5
| | 0 % ~calculating
|+++++ | 8 % ~00s
|+++++++++ | 17% ~00s
|+++++++++++++ | 25% ~00s
|+++++++++++++++++ | 33% ~00s
|+++++++++++++++++++++ | 42% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++ | 58% ~00s
|++++++++++++++++++++++++++++++++++ | 67% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 6
| | 0 % ~calculating
|+++++++ | 12% ~00s
|+++++++++++++ | 25% ~00s
|+++++++++++++++++++ | 38% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++++ | 62% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 786
Number of edges: 29724
Running Louvain algorithm...
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Maximum modularity in 10 random starts: 0.5897
Number of communities: 9
Elapsed time: 0 seconds
00:33:24 UMAP embedding parameters a = 0.9922 b = 1.112
00:33:24 Read 786 rows and found 30 numeric columns
00:33:24 Using Annoy for neighbor search, n_neighbors = 30
00:33:24 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:33:24 Writing NN index file to temp file /tmp/RtmpIqRk2o/file74f04154a3cea
00:33:24 Searching Annoy index using 1 thread, search_k = 3000
00:33:24 Annoy recall = 100%
00:33:24 Commencing smooth kNN distance calibration using 1 thread
00:33:25 Initializing from normalized Laplacian + noise
00:33:25 Commencing optimization for 500 epochs, with 29334 positive edges
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:33:26 Optimization finished
Calculating cluster 0
| | 0 % ~calculating
|+++++ | 9 % ~00s
|++++++++++ | 18% ~00s
|++++++++++++++ | 27% ~00s
|+++++++++++++++++++ | 36% ~00s
|+++++++++++++++++++++++ | 45% ~00s
|++++++++++++++++++++++++++++ | 55% ~00s
|++++++++++++++++++++++++++++++++ | 64% ~00s
|+++++++++++++++++++++++++++++++++++++ | 73% ~00s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 1
| | 0 % ~calculating
|+++++++++++++ | 25% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 2
| | 0 % ~calculating
|+++++ | 10% ~00s
|++++++++++ | 20% ~00s
|+++++++++++++++ | 30% ~00s
|++++++++++++++++++++ | 40% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++ | 60% ~00s
|+++++++++++++++++++++++++++++++++++ | 70% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 3
| | 0 % ~calculating
|++++ | 7 % ~00s
|++++++++ | 14% ~00s
|+++++++++++ | 21% ~00s
|+++++++++++++++ | 29% ~00s
|++++++++++++++++++ | 36% ~00s
|++++++++++++++++++++++ | 43% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|+++++++++++++++++++++++++++++ | 57% ~00s
|+++++++++++++++++++++++++++++++++ | 64% ~00s
|++++++++++++++++++++++++++++++++++++ | 71% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~00s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 4
| | 0 % ~calculating
|+++++ | 9 % ~00s
|++++++++++ | 18% ~00s
|++++++++++++++ | 27% ~00s
|+++++++++++++++++++ | 36% ~00s
|+++++++++++++++++++++++ | 45% ~00s
|++++++++++++++++++++++++++++ | 55% ~00s
|++++++++++++++++++++++++++++++++ | 64% ~00s
|+++++++++++++++++++++++++++++++++++++ | 73% ~00s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 5
| | 0 % ~calculating
|+++++ | 9 % ~00s
|++++++++++ | 18% ~00s
|++++++++++++++ | 27% ~00s
|+++++++++++++++++++ | 36% ~00s
|+++++++++++++++++++++++ | 45% ~00s
|++++++++++++++++++++++++++++ | 55% ~00s
|++++++++++++++++++++++++++++++++ | 64% ~00s
|+++++++++++++++++++++++++++++++++++++ | 73% ~00s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 6
| | 0 % ~calculating
|+++++ | 10% ~00s
|++++++++++ | 20% ~00s
|+++++++++++++++ | 30% ~00s
|++++++++++++++++++++ | 40% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++ | 60% ~00s
|+++++++++++++++++++++++++++++++++++ | 70% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 7
| | 0 % ~calculating
|+++++ | 8 % ~00s
|+++++++++ | 17% ~00s
|+++++++++++++ | 25% ~00s
|+++++++++++++++++ | 33% ~00s
|+++++++++++++++++++++ | 42% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++ | 58% ~00s
|++++++++++++++++++++++++++++++++++ | 67% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 8
| | 0 % ~calculating
|+++++++ | 12% ~00s
|+++++++++++++ | 25% ~00s
|+++++++++++++++++++ | 38% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++++ | 62% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 786
Number of edges: 29724
Running Louvain algorithm...
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Maximum modularity in 10 random starts: 0.5764
Number of communities: 9
Elapsed time: 0 seconds
00:33:27 UMAP embedding parameters a = 0.9922 b = 1.112
00:33:27 Read 786 rows and found 30 numeric columns
00:33:27 Using Annoy for neighbor search, n_neighbors = 30
00:33:27 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:33:27 Writing NN index file to temp file /tmp/RtmpIqRk2o/file74f045571094e
00:33:27 Searching Annoy index using 1 thread, search_k = 3000
00:33:27 Annoy recall = 100%
00:33:27 Commencing smooth kNN distance calibration using 1 thread
00:33:28 Initializing from normalized Laplacian + noise
00:33:28 Commencing optimization for 500 epochs, with 29334 positive edges
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:33:29 Optimization finished
Calculating cluster 0
| | 0 % ~calculating
|+++++ | 9 % ~00s
|++++++++++ | 18% ~00s
|++++++++++++++ | 27% ~00s
|+++++++++++++++++++ | 36% ~00s
|+++++++++++++++++++++++ | 45% ~00s
|++++++++++++++++++++++++++++ | 55% ~00s
|++++++++++++++++++++++++++++++++ | 64% ~00s
|+++++++++++++++++++++++++++++++++++++ | 73% ~00s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 1
| | 0 % ~calculating
|+++++++++++++ | 25% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 2
| | 0 % ~calculating
|++++ | 7 % ~00s
|++++++++ | 14% ~00s
|+++++++++++ | 21% ~00s
|+++++++++++++++ | 29% ~00s
|++++++++++++++++++ | 36% ~00s
|++++++++++++++++++++++ | 43% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|+++++++++++++++++++++++++++++ | 57% ~00s
|+++++++++++++++++++++++++++++++++ | 64% ~00s
|++++++++++++++++++++++++++++++++++++ | 71% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~00s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 3
| | 0 % ~calculating
|+++++ | 10% ~00s
|++++++++++ | 20% ~00s
|+++++++++++++++ | 30% ~00s
|++++++++++++++++++++ | 40% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++ | 60% ~00s
|+++++++++++++++++++++++++++++++++++ | 70% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 4
| | 0 % ~calculating
|+++++ | 9 % ~00s
|++++++++++ | 18% ~00s
|++++++++++++++ | 27% ~00s
|+++++++++++++++++++ | 36% ~00s
|+++++++++++++++++++++++ | 45% ~00s
|++++++++++++++++++++++++++++ | 55% ~00s
|++++++++++++++++++++++++++++++++ | 64% ~00s
|+++++++++++++++++++++++++++++++++++++ | 73% ~00s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 5
| | 0 % ~calculating
|+++++ | 9 % ~00s
|++++++++++ | 18% ~00s
|++++++++++++++ | 27% ~00s
|+++++++++++++++++++ | 36% ~00s
|+++++++++++++++++++++++ | 45% ~00s
|++++++++++++++++++++++++++++ | 55% ~00s
|++++++++++++++++++++++++++++++++ | 64% ~00s
|+++++++++++++++++++++++++++++++++++++ | 73% ~00s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 6
| | 0 % ~calculating
|+++++ | 10% ~00s
|++++++++++ | 20% ~00s
|+++++++++++++++ | 30% ~00s
|++++++++++++++++++++ | 40% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++ | 60% ~00s
|+++++++++++++++++++++++++++++++++++ | 70% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 7
| | 0 % ~calculating
|+++++ | 8 % ~00s
|+++++++++ | 17% ~00s
|+++++++++++++ | 25% ~00s
|+++++++++++++++++ | 33% ~00s
|+++++++++++++++++++++ | 42% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++ | 58% ~00s
|++++++++++++++++++++++++++++++++++ | 67% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 8
| | 0 % ~calculating
|+++++++ | 12% ~00s
|+++++++++++++ | 25% ~00s
|+++++++++++++++++++ | 38% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++++ | 62% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 786
Number of edges: 29724
Running Louvain algorithm...
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Maximum modularity in 10 random starts: 0.5635
Number of communities: 10
Elapsed time: 0 seconds
00:33:30 UMAP embedding parameters a = 0.9922 b = 1.112
00:33:30 Read 786 rows and found 30 numeric columns
00:33:30 Using Annoy for neighbor search, n_neighbors = 30
00:33:30 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:33:30 Writing NN index file to temp file /tmp/RtmpIqRk2o/file74f046209aee5
00:33:30 Searching Annoy index using 1 thread, search_k = 3000
00:33:30 Annoy recall = 100%
00:33:30 Commencing smooth kNN distance calibration using 1 thread
00:33:31 Initializing from normalized Laplacian + noise
00:33:31 Commencing optimization for 500 epochs, with 29334 positive edges
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:33:32 Optimization finished
Calculating cluster 0
| | 0 % ~calculating
|+++++ | 9 % ~00s
|++++++++++ | 18% ~00s
|++++++++++++++ | 27% ~00s
|+++++++++++++++++++ | 36% ~00s
|+++++++++++++++++++++++ | 45% ~00s
|++++++++++++++++++++++++++++ | 55% ~00s
|++++++++++++++++++++++++++++++++ | 64% ~00s
|+++++++++++++++++++++++++++++++++++++ | 73% ~00s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 1
| | 0 % ~calculating
|++++++++++ | 20% ~00s
|++++++++++++++++++++ | 40% ~00s
|++++++++++++++++++++++++++++++ | 60% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 2
| | 0 % ~calculating
|+++++ | 10% ~00s
|++++++++++ | 20% ~00s
|+++++++++++++++ | 30% ~00s
|++++++++++++++++++++ | 40% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++ | 60% ~00s
|+++++++++++++++++++++++++++++++++++ | 70% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 3
| | 0 % ~calculating
|+++++ | 9 % ~00s
|++++++++++ | 18% ~00s
|++++++++++++++ | 27% ~00s
|+++++++++++++++++++ | 36% ~00s
|+++++++++++++++++++++++ | 45% ~00s
|++++++++++++++++++++++++++++ | 55% ~00s
|++++++++++++++++++++++++++++++++ | 64% ~00s
|+++++++++++++++++++++++++++++++++++++ | 73% ~00s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 4
| | 0 % ~calculating
|+++++ | 8 % ~00s
|+++++++++ | 17% ~00s
|+++++++++++++ | 25% ~00s
|+++++++++++++++++ | 33% ~00s
|+++++++++++++++++++++ | 42% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++ | 58% ~00s
|++++++++++++++++++++++++++++++++++ | 67% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 5
| | 0 % ~calculating
|+++++ | 9 % ~00s
|++++++++++ | 18% ~00s
|++++++++++++++ | 27% ~00s
|+++++++++++++++++++ | 36% ~00s
|+++++++++++++++++++++++ | 45% ~00s
|++++++++++++++++++++++++++++ | 55% ~00s
|++++++++++++++++++++++++++++++++ | 64% ~00s
|+++++++++++++++++++++++++++++++++++++ | 73% ~00s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 6
| | 0 % ~calculating
|+++++ | 10% ~00s
|++++++++++ | 20% ~00s
|+++++++++++++++ | 30% ~00s
|++++++++++++++++++++ | 40% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++ | 60% ~00s
|+++++++++++++++++++++++++++++++++++ | 70% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 7
| | 0 % ~calculating
|+++++ | 8 % ~00s
|+++++++++ | 17% ~00s
|+++++++++++++ | 25% ~00s
|+++++++++++++++++ | 33% ~00s
|+++++++++++++++++++++ | 42% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++ | 58% ~00s
|++++++++++++++++++++++++++++++++++ | 67% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 8
| | 0 % ~calculating
|+++++++ | 12% ~00s
|+++++++++++++ | 25% ~00s
|+++++++++++++++++++ | 38% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++++ | 62% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 9
| | 0 % ~calculating
|++++++ | 11% ~00s
|++++++++++++ | 22% ~00s
|+++++++++++++++++ | 33% ~00s
|+++++++++++++++++++++++ | 44% ~00s
|++++++++++++++++++++++++++++ | 56% ~00s
|++++++++++++++++++++++++++++++++++ | 67% ~00s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 786
Number of edges: 29724
Running Louvain algorithm...
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Maximum modularity in 10 random starts: 0.5507
Number of communities: 10
Elapsed time: 0 seconds
00:33:33 UMAP embedding parameters a = 0.9922 b = 1.112
00:33:33 Read 786 rows and found 30 numeric columns
00:33:33 Using Annoy for neighbor search, n_neighbors = 30
00:33:33 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:33:33 Writing NN index file to temp file /tmp/RtmpIqRk2o/file74f04418e8957
00:33:33 Searching Annoy index using 1 thread, search_k = 3000
00:33:33 Annoy recall = 100%
00:33:33 Commencing smooth kNN distance calibration using 1 thread
00:33:34 Initializing from normalized Laplacian + noise
00:33:34 Commencing optimization for 500 epochs, with 29334 positive edges
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:33:35 Optimization finished
Calculating cluster 0
| | 0 % ~calculating
|+++++ | 9 % ~00s
|++++++++++ | 18% ~00s
|++++++++++++++ | 27% ~00s
|+++++++++++++++++++ | 36% ~00s
|+++++++++++++++++++++++ | 45% ~00s
|++++++++++++++++++++++++++++ | 55% ~00s
|++++++++++++++++++++++++++++++++ | 64% ~00s
|+++++++++++++++++++++++++++++++++++++ | 73% ~00s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 1
| | 0 % ~calculating
|++++++++++ | 20% ~00s
|++++++++++++++++++++ | 40% ~00s
|++++++++++++++++++++++++++++++ | 60% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 2
| | 0 % ~calculating
|+++++ | 10% ~00s
|++++++++++ | 20% ~00s
|+++++++++++++++ | 30% ~00s
|++++++++++++++++++++ | 40% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++ | 60% ~00s
|+++++++++++++++++++++++++++++++++++ | 70% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 3
| | 0 % ~calculating
|+++++ | 9 % ~00s
|++++++++++ | 18% ~00s
|++++++++++++++ | 27% ~00s
|+++++++++++++++++++ | 36% ~00s
|+++++++++++++++++++++++ | 45% ~00s
|++++++++++++++++++++++++++++ | 55% ~00s
|++++++++++++++++++++++++++++++++ | 64% ~00s
|+++++++++++++++++++++++++++++++++++++ | 73% ~00s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 4
| | 0 % ~calculating
|+++++ | 9 % ~00s
|++++++++++ | 18% ~00s
|++++++++++++++ | 27% ~00s
|+++++++++++++++++++ | 36% ~00s
|+++++++++++++++++++++++ | 45% ~00s
|++++++++++++++++++++++++++++ | 55% ~00s
|++++++++++++++++++++++++++++++++ | 64% ~00s
|+++++++++++++++++++++++++++++++++++++ | 73% ~00s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 5
| | 0 % ~calculating
|+++++ | 10% ~00s
|++++++++++ | 20% ~00s
|+++++++++++++++ | 30% ~00s
|++++++++++++++++++++ | 40% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++ | 60% ~00s
|+++++++++++++++++++++++++++++++++++ | 70% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 6
| | 0 % ~calculating
|+++++ | 8 % ~00s
|+++++++++ | 17% ~00s
|+++++++++++++ | 25% ~00s
|+++++++++++++++++ | 33% ~00s
|+++++++++++++++++++++ | 42% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++ | 58% ~00s
|++++++++++++++++++++++++++++++++++ | 67% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 7
| | 0 % ~calculating
|++++ | 8 % ~00s
|++++++++ | 15% ~00s
|++++++++++++ | 23% ~00s
|++++++++++++++++ | 31% ~00s
|++++++++++++++++++++ | 38% ~00s
|++++++++++++++++++++++++ | 46% ~00s
|+++++++++++++++++++++++++++ | 54% ~00s
|+++++++++++++++++++++++++++++++ | 62% ~00s
|+++++++++++++++++++++++++++++++++++ | 69% ~00s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~00s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 8
| | 0 % ~calculating
|+++++ | 8 % ~00s
|+++++++++ | 17% ~00s
|+++++++++++++ | 25% ~00s
|+++++++++++++++++ | 33% ~00s
|+++++++++++++++++++++ | 42% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++ | 58% ~00s
|++++++++++++++++++++++++++++++++++ | 67% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 9
| | 0 % ~calculating
|+++++++ | 12% ~00s
|+++++++++++++ | 25% ~00s
|+++++++++++++++++++ | 38% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++++ | 62% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 786
Number of edges: 29724
Running Louvain algorithm...
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Maximum modularity in 10 random starts: 0.5380
Number of communities: 10
Elapsed time: 0 seconds
00:33:36 UMAP embedding parameters a = 0.9922 b = 1.112
00:33:36 Read 786 rows and found 30 numeric columns
00:33:36 Using Annoy for neighbor search, n_neighbors = 30
00:33:36 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:33:36 Writing NN index file to temp file /tmp/RtmpIqRk2o/file74f0424306e45
00:33:36 Searching Annoy index using 1 thread, search_k = 3000
00:33:36 Annoy recall = 100%
00:33:36 Commencing smooth kNN distance calibration using 1 thread
00:33:37 Initializing from normalized Laplacian + noise
00:33:37 Commencing optimization for 500 epochs, with 29334 positive edges
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:33:38 Optimization finished
Calculating cluster 0
| | 0 % ~calculating
|+++++ | 9 % ~00s
|++++++++++ | 18% ~00s
|++++++++++++++ | 27% ~00s
|+++++++++++++++++++ | 36% ~00s
|+++++++++++++++++++++++ | 45% ~00s
|++++++++++++++++++++++++++++ | 55% ~00s
|++++++++++++++++++++++++++++++++ | 64% ~00s
|+++++++++++++++++++++++++++++++++++++ | 73% ~00s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 1
| | 0 % ~calculating
|++++++++++ | 20% ~00s
|++++++++++++++++++++ | 40% ~00s
|++++++++++++++++++++++++++++++ | 60% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 2
| | 0 % ~calculating
|+++++ | 10% ~00s
|++++++++++ | 20% ~00s
|+++++++++++++++ | 30% ~00s
|++++++++++++++++++++ | 40% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++ | 60% ~00s
|+++++++++++++++++++++++++++++++++++ | 70% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 3
| | 0 % ~calculating
|+++++ | 8 % ~00s
|+++++++++ | 17% ~00s
|+++++++++++++ | 25% ~00s
|+++++++++++++++++ | 33% ~00s
|+++++++++++++++++++++ | 42% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++ | 58% ~00s
|++++++++++++++++++++++++++++++++++ | 67% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 4
| | 0 % ~calculating
|+++++ | 9 % ~00s
|++++++++++ | 18% ~00s
|++++++++++++++ | 27% ~00s
|+++++++++++++++++++ | 36% ~00s
|+++++++++++++++++++++++ | 45% ~00s
|++++++++++++++++++++++++++++ | 55% ~00s
|++++++++++++++++++++++++++++++++ | 64% ~00s
|+++++++++++++++++++++++++++++++++++++ | 73% ~00s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 5
| | 0 % ~calculating
|+++++ | 10% ~00s
|++++++++++ | 20% ~00s
|+++++++++++++++ | 30% ~00s
|++++++++++++++++++++ | 40% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++ | 60% ~00s
|+++++++++++++++++++++++++++++++++++ | 70% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 6
| | 0 % ~calculating
|+++++ | 8 % ~00s
|+++++++++ | 17% ~00s
|+++++++++++++ | 25% ~00s
|+++++++++++++++++ | 33% ~00s
|+++++++++++++++++++++ | 42% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++ | 58% ~00s
|++++++++++++++++++++++++++++++++++ | 67% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 7
| | 0 % ~calculating
|++++ | 8 % ~00s
|++++++++ | 15% ~00s
|++++++++++++ | 23% ~00s
|++++++++++++++++ | 31% ~00s
|++++++++++++++++++++ | 38% ~00s
|++++++++++++++++++++++++ | 46% ~00s
|+++++++++++++++++++++++++++ | 54% ~00s
|+++++++++++++++++++++++++++++++ | 62% ~00s
|+++++++++++++++++++++++++++++++++++ | 69% ~00s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~00s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 8
| | 0 % ~calculating
|+++++ | 8 % ~00s
|+++++++++ | 17% ~00s
|+++++++++++++ | 25% ~00s
|+++++++++++++++++ | 33% ~00s
|+++++++++++++++++++++ | 42% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++ | 58% ~00s
|++++++++++++++++++++++++++++++++++ | 67% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 9
| | 0 % ~calculating
|+++++++ | 12% ~00s
|+++++++++++++ | 25% ~00s
|+++++++++++++++++++ | 38% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++++ | 62% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 786
Number of edges: 29724
Running Louvain algorithm...
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Maximum modularity in 10 random starts: 0.5250
Number of communities: 11
Elapsed time: 0 seconds
00:33:39 UMAP embedding parameters a = 0.9922 b = 1.112
00:33:39 Read 786 rows and found 30 numeric columns
00:33:39 Using Annoy for neighbor search, n_neighbors = 30
00:33:39 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:33:39 Writing NN index file to temp file /tmp/RtmpIqRk2o/file74f0427a7e0aa
00:33:39 Searching Annoy index using 1 thread, search_k = 3000
00:33:39 Annoy recall = 100%
00:33:39 Commencing smooth kNN distance calibration using 1 thread
00:33:40 Initializing from normalized Laplacian + noise
00:33:40 Commencing optimization for 500 epochs, with 29334 positive edges
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:33:41 Optimization finished
Calculating cluster 0
| | 0 % ~calculating
|++++++++++ | 20% ~00s
|++++++++++++++++++++ | 40% ~00s
|++++++++++++++++++++++++++++++ | 60% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 1
| | 0 % ~calculating
|+++++ | 10% ~00s
|++++++++++ | 20% ~00s
|+++++++++++++++ | 30% ~00s
|++++++++++++++++++++ | 40% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++ | 60% ~00s
|+++++++++++++++++++++++++++++++++++ | 70% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 2
| | 0 % ~calculating
|+++++ | 9 % ~00s
|++++++++++ | 18% ~00s
|++++++++++++++ | 27% ~00s
|+++++++++++++++++++ | 36% ~00s
|+++++++++++++++++++++++ | 45% ~00s
|++++++++++++++++++++++++++++ | 55% ~00s
|++++++++++++++++++++++++++++++++ | 64% ~00s
|+++++++++++++++++++++++++++++++++++++ | 73% ~00s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 3
| | 0 % ~calculating
|+++++++ | 12% ~00s
|+++++++++++++ | 25% ~00s
|+++++++++++++++++++ | 38% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++++ | 62% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 4
| | 0 % ~calculating
|+++++ | 9 % ~00s
|++++++++++ | 18% ~00s
|++++++++++++++ | 27% ~00s
|+++++++++++++++++++ | 36% ~00s
|+++++++++++++++++++++++ | 45% ~00s
|++++++++++++++++++++++++++++ | 55% ~00s
|++++++++++++++++++++++++++++++++ | 64% ~00s
|+++++++++++++++++++++++++++++++++++++ | 73% ~00s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 5
| | 0 % ~calculating
|+++++ | 10% ~00s
|++++++++++ | 20% ~00s
|+++++++++++++++ | 30% ~00s
|++++++++++++++++++++ | 40% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++ | 60% ~00s
|+++++++++++++++++++++++++++++++++++ | 70% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 6
| | 0 % ~calculating
|++++ | 8 % ~00s
|++++++++ | 15% ~00s
|++++++++++++ | 23% ~00s
|++++++++++++++++ | 31% ~00s
|++++++++++++++++++++ | 38% ~00s
|++++++++++++++++++++++++ | 46% ~00s
|+++++++++++++++++++++++++++ | 54% ~00s
|+++++++++++++++++++++++++++++++ | 62% ~00s
|+++++++++++++++++++++++++++++++++++ | 69% ~00s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~00s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 7
| | 0 % ~calculating
|+++++ | 8 % ~00s
|+++++++++ | 17% ~00s
|+++++++++++++ | 25% ~00s
|+++++++++++++++++ | 33% ~00s
|+++++++++++++++++++++ | 42% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++ | 58% ~00s
|++++++++++++++++++++++++++++++++++ | 67% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 8
| | 0 % ~calculating
|+++++ | 8 % ~00s
|+++++++++ | 17% ~00s
|+++++++++++++ | 25% ~00s
|+++++++++++++++++ | 33% ~00s
|+++++++++++++++++++++ | 42% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++ | 58% ~00s
|++++++++++++++++++++++++++++++++++ | 67% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 9
| | 0 % ~calculating
|+++++++ | 12% ~00s
|+++++++++++++ | 25% ~00s
|+++++++++++++++++++ | 38% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++++ | 62% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 10
| | 0 % ~calculating
|+++++ | 8 % ~00s
|+++++++++ | 17% ~00s
|+++++++++++++ | 25% ~00s
|+++++++++++++++++ | 33% ~00s
|+++++++++++++++++++++ | 42% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++ | 58% ~00s
|++++++++++++++++++++++++++++++++++ | 67% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 786
Number of edges: 29724
Running Louvain algorithm...
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Maximum modularity in 10 random starts: 0.5139
Number of communities: 11
Elapsed time: 0 seconds
00:33:42 UMAP embedding parameters a = 0.9922 b = 1.112
00:33:42 Read 786 rows and found 30 numeric columns
00:33:42 Using Annoy for neighbor search, n_neighbors = 30
00:33:42 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:33:42 Writing NN index file to temp file /tmp/RtmpIqRk2o/file74f0428ceba0c
00:33:42 Searching Annoy index using 1 thread, search_k = 3000
00:33:42 Annoy recall = 100%
00:33:43 Commencing smooth kNN distance calibration using 1 thread
00:33:43 Initializing from normalized Laplacian + noise
00:33:43 Commencing optimization for 500 epochs, with 29334 positive edges
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:33:44 Optimization finished
Calculating cluster 0
| | 0 % ~calculating
|++++++++++ | 20% ~00s
|++++++++++++++++++++ | 40% ~00s
|++++++++++++++++++++++++++++++ | 60% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 1
| | 0 % ~calculating
|+++++ | 10% ~00s
|++++++++++ | 20% ~00s
|+++++++++++++++ | 30% ~00s
|++++++++++++++++++++ | 40% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++ | 60% ~00s
|+++++++++++++++++++++++++++++++++++ | 70% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 2
| | 0 % ~calculating
|+++++ | 8 % ~00s
|+++++++++ | 17% ~00s
|+++++++++++++ | 25% ~00s
|+++++++++++++++++ | 33% ~00s
|+++++++++++++++++++++ | 42% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++ | 58% ~00s
|++++++++++++++++++++++++++++++++++ | 67% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 3
| | 0 % ~calculating
|+++++++ | 12% ~00s
|+++++++++++++ | 25% ~00s
|+++++++++++++++++++ | 38% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++++ | 62% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 4
| | 0 % ~calculating
|+++++ | 9 % ~00s
|++++++++++ | 18% ~00s
|++++++++++++++ | 27% ~00s
|+++++++++++++++++++ | 36% ~00s
|+++++++++++++++++++++++ | 45% ~00s
|++++++++++++++++++++++++++++ | 55% ~00s
|++++++++++++++++++++++++++++++++ | 64% ~00s
|+++++++++++++++++++++++++++++++++++++ | 73% ~00s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 5
| | 0 % ~calculating
|+++++ | 10% ~00s
|++++++++++ | 20% ~00s
|+++++++++++++++ | 30% ~00s
|++++++++++++++++++++ | 40% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++ | 60% ~00s
|+++++++++++++++++++++++++++++++++++ | 70% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 6
| | 0 % ~calculating
|++++ | 8 % ~00s
|++++++++ | 15% ~00s
|++++++++++++ | 23% ~00s
|++++++++++++++++ | 31% ~00s
|++++++++++++++++++++ | 38% ~00s
|++++++++++++++++++++++++ | 46% ~00s
|+++++++++++++++++++++++++++ | 54% ~00s
|+++++++++++++++++++++++++++++++ | 62% ~00s
|+++++++++++++++++++++++++++++++++++ | 69% ~00s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~00s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 7
| | 0 % ~calculating
|+++++ | 8 % ~00s
|+++++++++ | 17% ~00s
|+++++++++++++ | 25% ~00s
|+++++++++++++++++ | 33% ~00s
|+++++++++++++++++++++ | 42% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++ | 58% ~00s
|++++++++++++++++++++++++++++++++++ | 67% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 8
| | 0 % ~calculating
|+++++ | 8 % ~00s
|+++++++++ | 17% ~00s
|+++++++++++++ | 25% ~00s
|+++++++++++++++++ | 33% ~00s
|+++++++++++++++++++++ | 42% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++ | 58% ~00s
|++++++++++++++++++++++++++++++++++ | 67% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 9
| | 0 % ~calculating
|+++++++ | 12% ~00s
|+++++++++++++ | 25% ~00s
|+++++++++++++++++++ | 38% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++++ | 62% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 10
| | 0 % ~calculating
|+++++ | 8 % ~00s
|+++++++++ | 17% ~00s
|+++++++++++++ | 25% ~00s
|+++++++++++++++++ | 33% ~00s
|+++++++++++++++++++++ | 42% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++ | 58% ~00s
|++++++++++++++++++++++++++++++++++ | 67% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 786
Number of edges: 29724
Running Louvain algorithm...
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Maximum modularity in 10 random starts: 0.5031
Number of communities: 11
Elapsed time: 0 seconds
00:33:45 UMAP embedding parameters a = 0.9922 b = 1.112
00:33:45 Read 786 rows and found 30 numeric columns
00:33:45 Using Annoy for neighbor search, n_neighbors = 30
00:33:45 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:33:45 Writing NN index file to temp file /tmp/RtmpIqRk2o/file74f044337e03d
00:33:45 Searching Annoy index using 1 thread, search_k = 3000
00:33:45 Annoy recall = 100%
00:33:46 Commencing smooth kNN distance calibration using 1 thread
00:33:46 Initializing from normalized Laplacian + noise
00:33:46 Commencing optimization for 500 epochs, with 29334 positive edges
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:33:47 Optimization finished
Calculating cluster 0
| | 0 % ~calculating
|++++++++++ | 20% ~00s
|++++++++++++++++++++ | 40% ~00s
|++++++++++++++++++++++++++++++ | 60% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 1
| | 0 % ~calculating
|+++++ | 10% ~00s
|++++++++++ | 20% ~00s
|+++++++++++++++ | 30% ~00s
|++++++++++++++++++++ | 40% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++ | 60% ~00s
|+++++++++++++++++++++++++++++++++++ | 70% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 2
| | 0 % ~calculating
|+++++ | 8 % ~00s
|+++++++++ | 17% ~00s
|+++++++++++++ | 25% ~00s
|+++++++++++++++++ | 33% ~00s
|+++++++++++++++++++++ | 42% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++ | 58% ~00s
|++++++++++++++++++++++++++++++++++ | 67% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 3
| | 0 % ~calculating
|+++++++ | 12% ~00s
|+++++++++++++ | 25% ~00s
|+++++++++++++++++++ | 38% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++++ | 62% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 4
| | 0 % ~calculating
|+++++ | 9 % ~00s
|++++++++++ | 18% ~00s
|++++++++++++++ | 27% ~00s
|+++++++++++++++++++ | 36% ~00s
|+++++++++++++++++++++++ | 45% ~00s
|++++++++++++++++++++++++++++ | 55% ~00s
|++++++++++++++++++++++++++++++++ | 64% ~00s
|+++++++++++++++++++++++++++++++++++++ | 73% ~00s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 5
| | 0 % ~calculating
|+++++ | 9 % ~00s
|++++++++++ | 18% ~00s
|++++++++++++++ | 27% ~00s
|+++++++++++++++++++ | 36% ~00s
|+++++++++++++++++++++++ | 45% ~00s
|++++++++++++++++++++++++++++ | 55% ~00s
|++++++++++++++++++++++++++++++++ | 64% ~00s
|+++++++++++++++++++++++++++++++++++++ | 73% ~00s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 6
| | 0 % ~calculating
|+++++ | 10% ~00s
|++++++++++ | 20% ~00s
|+++++++++++++++ | 30% ~00s
|++++++++++++++++++++ | 40% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++ | 60% ~00s
|+++++++++++++++++++++++++++++++++++ | 70% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 7
| | 0 % ~calculating
|++++ | 7 % ~00s
|++++++++ | 14% ~00s
|+++++++++++ | 21% ~00s
|+++++++++++++++ | 29% ~00s
|++++++++++++++++++ | 36% ~00s
|++++++++++++++++++++++ | 43% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|+++++++++++++++++++++++++++++ | 57% ~00s
|+++++++++++++++++++++++++++++++++ | 64% ~00s
|++++++++++++++++++++++++++++++++++++ | 71% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~00s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 8
| | 0 % ~calculating
|+++++ | 8 % ~00s
|+++++++++ | 17% ~00s
|+++++++++++++ | 25% ~00s
|+++++++++++++++++ | 33% ~00s
|+++++++++++++++++++++ | 42% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++ | 58% ~00s
|++++++++++++++++++++++++++++++++++ | 67% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 9
| | 0 % ~calculating
|+++++++ | 12% ~00s
|+++++++++++++ | 25% ~00s
|+++++++++++++++++++ | 38% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++++ | 62% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 10
| | 0 % ~calculating
|+++++ | 8 % ~00s
|+++++++++ | 17% ~00s
|+++++++++++++ | 25% ~00s
|+++++++++++++++++ | 33% ~00s
|+++++++++++++++++++++ | 42% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++ | 58% ~00s
|++++++++++++++++++++++++++++++++++ | 67% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 786
Number of edges: 29724
Running Louvain algorithm...
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Maximum modularity in 10 random starts: 0.4922
Number of communities: 11
Elapsed time: 0 seconds
00:33:48 UMAP embedding parameters a = 0.9922 b = 1.112
00:33:48 Read 786 rows and found 30 numeric columns
00:33:48 Using Annoy for neighbor search, n_neighbors = 30
00:33:48 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:33:48 Writing NN index file to temp file /tmp/RtmpIqRk2o/file74f044b2835a6
00:33:48 Searching Annoy index using 1 thread, search_k = 3000
00:33:48 Annoy recall = 100%
00:33:49 Commencing smooth kNN distance calibration using 1 thread
00:33:49 Initializing from normalized Laplacian + noise
00:33:49 Commencing optimization for 500 epochs, with 29334 positive edges
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:33:50 Optimization finished
Calculating cluster 0
| | 0 % ~calculating
|++++++++++ | 20% ~00s
|++++++++++++++++++++ | 40% ~00s
|++++++++++++++++++++++++++++++ | 60% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 1
| | 0 % ~calculating
|+++++ | 10% ~00s
|++++++++++ | 20% ~00s
|+++++++++++++++ | 30% ~00s
|++++++++++++++++++++ | 40% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++ | 60% ~00s
|+++++++++++++++++++++++++++++++++++ | 70% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 2
| | 0 % ~calculating
|+++++ | 8 % ~00s
|+++++++++ | 17% ~00s
|+++++++++++++ | 25% ~00s
|+++++++++++++++++ | 33% ~00s
|+++++++++++++++++++++ | 42% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++ | 58% ~00s
|++++++++++++++++++++++++++++++++++ | 67% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 3
| | 0 % ~calculating
|++++++++ | 14% ~00s
|+++++++++++++++ | 29% ~00s
|++++++++++++++++++++++ | 43% ~00s
|+++++++++++++++++++++++++++++ | 57% ~00s
|++++++++++++++++++++++++++++++++++++ | 71% ~00s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 4
| | 0 % ~calculating
|+++++ | 9 % ~00s
|++++++++++ | 18% ~00s
|++++++++++++++ | 27% ~00s
|+++++++++++++++++++ | 36% ~00s
|+++++++++++++++++++++++ | 45% ~00s
|++++++++++++++++++++++++++++ | 55% ~00s
|++++++++++++++++++++++++++++++++ | 64% ~00s
|+++++++++++++++++++++++++++++++++++++ | 73% ~00s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 5
| | 0 % ~calculating
|+++++ | 9 % ~00s
|++++++++++ | 18% ~00s
|++++++++++++++ | 27% ~00s
|+++++++++++++++++++ | 36% ~00s
|+++++++++++++++++++++++ | 45% ~00s
|++++++++++++++++++++++++++++ | 55% ~00s
|++++++++++++++++++++++++++++++++ | 64% ~00s
|+++++++++++++++++++++++++++++++++++++ | 73% ~00s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 6
| | 0 % ~calculating
|+++++ | 10% ~00s
|++++++++++ | 20% ~00s
|+++++++++++++++ | 30% ~00s
|++++++++++++++++++++ | 40% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++ | 60% ~00s
|+++++++++++++++++++++++++++++++++++ | 70% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 7
| | 0 % ~calculating
|++++ | 7 % ~00s
|++++++++ | 14% ~00s
|+++++++++++ | 21% ~00s
|+++++++++++++++ | 29% ~00s
|++++++++++++++++++ | 36% ~00s
|++++++++++++++++++++++ | 43% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|+++++++++++++++++++++++++++++ | 57% ~00s
|+++++++++++++++++++++++++++++++++ | 64% ~00s
|++++++++++++++++++++++++++++++++++++ | 71% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~00s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 8
| | 0 % ~calculating
|+++++ | 8 % ~00s
|+++++++++ | 17% ~00s
|+++++++++++++ | 25% ~00s
|+++++++++++++++++ | 33% ~00s
|+++++++++++++++++++++ | 42% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++ | 58% ~00s
|++++++++++++++++++++++++++++++++++ | 67% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 9
| | 0 % ~calculating
|++++++ | 11% ~00s
|++++++++++++ | 22% ~00s
|+++++++++++++++++ | 33% ~00s
|+++++++++++++++++++++++ | 44% ~00s
|++++++++++++++++++++++++++++ | 56% ~00s
|++++++++++++++++++++++++++++++++++ | 67% ~00s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 10
| | 0 % ~calculating
|+++++ | 8 % ~00s
|+++++++++ | 17% ~00s
|+++++++++++++ | 25% ~00s
|+++++++++++++++++ | 33% ~00s
|+++++++++++++++++++++ | 42% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++ | 58% ~00s
|++++++++++++++++++++++++++++++++++ | 67% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 786
Number of edges: 29724
Running Louvain algorithm...
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Maximum modularity in 10 random starts: 0.4812
Number of communities: 12
Elapsed time: 0 seconds
1 singletons identified. 11 final clusters.
00:33:51 UMAP embedding parameters a = 0.9922 b = 1.112
00:33:51 Read 786 rows and found 30 numeric columns
00:33:51 Using Annoy for neighbor search, n_neighbors = 30
00:33:51 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:33:51 Writing NN index file to temp file /tmp/RtmpIqRk2o/file74f0457c42863
00:33:51 Searching Annoy index using 1 thread, search_k = 3000
00:33:51 Annoy recall = 100%
00:33:52 Commencing smooth kNN distance calibration using 1 thread
00:33:52 Initializing from normalized Laplacian + noise
00:33:53 Commencing optimization for 500 epochs, with 29334 positive edges
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:33:54 Optimization finished
Calculating cluster 0
| | 0 % ~calculating
|+++++++++++++ | 25% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 1
| | 0 % ~calculating
|+++++ | 10% ~00s
|++++++++++ | 20% ~00s
|+++++++++++++++ | 30% ~00s
|++++++++++++++++++++ | 40% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++ | 60% ~00s
|+++++++++++++++++++++++++++++++++++ | 70% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 2
| | 0 % ~calculating
|+++++ | 9 % ~00s
|++++++++++ | 18% ~00s
|++++++++++++++ | 27% ~00s
|+++++++++++++++++++ | 36% ~00s
|+++++++++++++++++++++++ | 45% ~00s
|++++++++++++++++++++++++++++ | 55% ~00s
|++++++++++++++++++++++++++++++++ | 64% ~00s
|+++++++++++++++++++++++++++++++++++++ | 73% ~00s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 3
| | 0 % ~calculating
|+++++++ | 12% ~00s
|+++++++++++++ | 25% ~00s
|+++++++++++++++++++ | 38% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++++ | 62% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 4
| | 0 % ~calculating
|+++++ | 9 % ~00s
|++++++++++ | 18% ~00s
|++++++++++++++ | 27% ~00s
|+++++++++++++++++++ | 36% ~00s
|+++++++++++++++++++++++ | 45% ~00s
|++++++++++++++++++++++++++++ | 55% ~00s
|++++++++++++++++++++++++++++++++ | 64% ~00s
|+++++++++++++++++++++++++++++++++++++ | 73% ~00s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 5
| | 0 % ~calculating
|+++++ | 9 % ~00s
|++++++++++ | 18% ~00s
|++++++++++++++ | 27% ~00s
|+++++++++++++++++++ | 36% ~00s
|+++++++++++++++++++++++ | 45% ~00s
|++++++++++++++++++++++++++++ | 55% ~00s
|++++++++++++++++++++++++++++++++ | 64% ~00s
|+++++++++++++++++++++++++++++++++++++ | 73% ~00s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 6
| | 0 % ~calculating
|+++++ | 9 % ~00s
|++++++++++ | 18% ~00s
|++++++++++++++ | 27% ~00s
|+++++++++++++++++++ | 36% ~00s
|+++++++++++++++++++++++ | 45% ~00s
|++++++++++++++++++++++++++++ | 55% ~00s
|++++++++++++++++++++++++++++++++ | 64% ~00s
|+++++++++++++++++++++++++++++++++++++ | 73% ~00s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 7
| | 0 % ~calculating
|++++ | 7 % ~00s
|++++++++ | 14% ~00s
|+++++++++++ | 21% ~00s
|+++++++++++++++ | 29% ~00s
|++++++++++++++++++ | 36% ~00s
|++++++++++++++++++++++ | 43% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|+++++++++++++++++++++++++++++ | 57% ~00s
|+++++++++++++++++++++++++++++++++ | 64% ~00s
|++++++++++++++++++++++++++++++++++++ | 71% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~00s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 8
| | 0 % ~calculating
|+++++ | 8 % ~00s
|+++++++++ | 17% ~00s
|+++++++++++++ | 25% ~00s
|+++++++++++++++++ | 33% ~00s
|+++++++++++++++++++++ | 42% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++ | 58% ~00s
|++++++++++++++++++++++++++++++++++ | 67% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 9
| | 0 % ~calculating
|++++++ | 11% ~00s
|++++++++++++ | 22% ~00s
|+++++++++++++++++ | 33% ~00s
|+++++++++++++++++++++++ | 44% ~00s
|++++++++++++++++++++++++++++ | 56% ~00s
|++++++++++++++++++++++++++++++++++ | 67% ~00s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 10
| | 0 % ~calculating
|+++++ | 8 % ~00s
|+++++++++ | 17% ~00s
|+++++++++++++ | 25% ~00s
|+++++++++++++++++ | 33% ~00s
|+++++++++++++++++++++ | 42% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++ | 58% ~00s
|++++++++++++++++++++++++++++++++++ | 67% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 786
Number of edges: 29724
Running Louvain algorithm...
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Maximum modularity in 10 random starts: 0.4704
Number of communities: 13
Elapsed time: 0 seconds
1 singletons identified. 12 final clusters.
00:33:54 UMAP embedding parameters a = 0.9922 b = 1.112
00:33:54 Read 786 rows and found 30 numeric columns
00:33:54 Using Annoy for neighbor search, n_neighbors = 30
00:33:54 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:33:54 Writing NN index file to temp file /tmp/RtmpIqRk2o/file74f0447bc00b8
00:33:54 Searching Annoy index using 1 thread, search_k = 3000
00:33:55 Annoy recall = 100%
00:33:55 Commencing smooth kNN distance calibration using 1 thread
00:33:56 Initializing from normalized Laplacian + noise
00:33:56 Commencing optimization for 500 epochs, with 29334 positive edges
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:33:57 Optimization finished
Calculating cluster 0
| | 0 % ~calculating
|+++++++++++++ | 25% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 1
| | 0 % ~calculating
|+++++ | 10% ~00s
|++++++++++ | 20% ~00s
|+++++++++++++++ | 30% ~00s
|++++++++++++++++++++ | 40% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++ | 60% ~00s
|+++++++++++++++++++++++++++++++++++ | 70% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 2
| | 0 % ~calculating
|+++++ | 9 % ~00s
|++++++++++ | 18% ~00s
|++++++++++++++ | 27% ~00s
|+++++++++++++++++++ | 36% ~00s
|+++++++++++++++++++++++ | 45% ~00s
|++++++++++++++++++++++++++++ | 55% ~00s
|++++++++++++++++++++++++++++++++ | 64% ~00s
|+++++++++++++++++++++++++++++++++++++ | 73% ~00s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 3
| | 0 % ~calculating
|+++++++ | 12% ~00s
|+++++++++++++ | 25% ~00s
|+++++++++++++++++++ | 38% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++++ | 62% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 4
| | 0 % ~calculating
|+++++ | 9 % ~00s
|++++++++++ | 18% ~00s
|++++++++++++++ | 27% ~00s
|+++++++++++++++++++ | 36% ~00s
|+++++++++++++++++++++++ | 45% ~00s
|++++++++++++++++++++++++++++ | 55% ~00s
|++++++++++++++++++++++++++++++++ | 64% ~00s
|+++++++++++++++++++++++++++++++++++++ | 73% ~00s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 5
| | 0 % ~calculating
|+++++ | 9 % ~00s
|++++++++++ | 18% ~00s
|++++++++++++++ | 27% ~00s
|+++++++++++++++++++ | 36% ~00s
|+++++++++++++++++++++++ | 45% ~00s
|++++++++++++++++++++++++++++ | 55% ~00s
|++++++++++++++++++++++++++++++++ | 64% ~00s
|+++++++++++++++++++++++++++++++++++++ | 73% ~00s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 6
| | 0 % ~calculating
|+++++ | 9 % ~00s
|++++++++++ | 18% ~00s
|++++++++++++++ | 27% ~00s
|+++++++++++++++++++ | 36% ~00s
|+++++++++++++++++++++++ | 45% ~00s
|++++++++++++++++++++++++++++ | 55% ~00s
|++++++++++++++++++++++++++++++++ | 64% ~00s
|+++++++++++++++++++++++++++++++++++++ | 73% ~00s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 7
| | 0 % ~calculating
|+++++ | 8 % ~00s
|+++++++++ | 17% ~00s
|+++++++++++++ | 25% ~00s
|+++++++++++++++++ | 33% ~00s
|+++++++++++++++++++++ | 42% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++ | 58% ~00s
|++++++++++++++++++++++++++++++++++ | 67% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 8
| | 0 % ~calculating
|++++++ | 11% ~00s
|++++++++++++ | 22% ~00s
|+++++++++++++++++ | 33% ~00s
|+++++++++++++++++++++++ | 44% ~00s
|++++++++++++++++++++++++++++ | 56% ~00s
|++++++++++++++++++++++++++++++++++ | 67% ~00s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 9
| | 0 % ~calculating
|+++++ | 8 % ~00s
|+++++++++ | 17% ~00s
|+++++++++++++ | 25% ~00s
|+++++++++++++++++ | 33% ~00s
|+++++++++++++++++++++ | 42% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++ | 58% ~00s
|++++++++++++++++++++++++++++++++++ | 67% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 10
| | 0 % ~calculating
|++++ | 7 % ~00s
|++++++++ | 14% ~00s
|+++++++++++ | 21% ~00s
|+++++++++++++++ | 29% ~00s
|++++++++++++++++++ | 36% ~00s
|++++++++++++++++++++++ | 43% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|+++++++++++++++++++++++++++++ | 57% ~00s
|+++++++++++++++++++++++++++++++++ | 64% ~00s
|++++++++++++++++++++++++++++++++++++ | 71% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~00s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 11
| | 0 % ~calculating
|++++++ | 11% ~00s
|++++++++++++ | 22% ~00s
|+++++++++++++++++ | 33% ~00s
|+++++++++++++++++++++++ | 44% ~00s
|++++++++++++++++++++++++++++ | 56% ~00s
|++++++++++++++++++++++++++++++++++ | 67% ~00s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 786
Number of edges: 29724
Running Louvain algorithm...
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Maximum modularity in 10 random starts: 0.4600
Number of communities: 12
Elapsed time: 0 seconds
00:33:57 UMAP embedding parameters a = 0.9922 b = 1.112
00:33:57 Read 786 rows and found 30 numeric columns
00:33:57 Using Annoy for neighbor search, n_neighbors = 30
00:33:57 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:33:58 Writing NN index file to temp file /tmp/RtmpIqRk2o/file74f0435b8e4be
00:33:58 Searching Annoy index using 1 thread, search_k = 3000
00:33:58 Annoy recall = 100%
00:33:58 Commencing smooth kNN distance calibration using 1 thread
00:33:59 Initializing from normalized Laplacian + noise
00:33:59 Commencing optimization for 500 epochs, with 29334 positive edges
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:34:00 Optimization finished
Calculating cluster 0
| | 0 % ~calculating
|+++++++++++++ | 25% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 1
| | 0 % ~calculating
|+++++ | 10% ~00s
|++++++++++ | 20% ~00s
|+++++++++++++++ | 30% ~00s
|++++++++++++++++++++ | 40% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++ | 60% ~00s
|+++++++++++++++++++++++++++++++++++ | 70% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 2
| | 0 % ~calculating
|+++++ | 9 % ~00s
|++++++++++ | 18% ~00s
|++++++++++++++ | 27% ~00s
|+++++++++++++++++++ | 36% ~00s
|+++++++++++++++++++++++ | 45% ~00s
|++++++++++++++++++++++++++++ | 55% ~00s
|++++++++++++++++++++++++++++++++ | 64% ~00s
|+++++++++++++++++++++++++++++++++++++ | 73% ~00s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 3
| | 0 % ~calculating
|+++++++ | 12% ~00s
|+++++++++++++ | 25% ~00s
|+++++++++++++++++++ | 38% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++++ | 62% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 4
| | 0 % ~calculating
|+++++ | 9 % ~00s
|++++++++++ | 18% ~00s
|++++++++++++++ | 27% ~00s
|+++++++++++++++++++ | 36% ~00s
|+++++++++++++++++++++++ | 45% ~00s
|++++++++++++++++++++++++++++ | 55% ~00s
|++++++++++++++++++++++++++++++++ | 64% ~00s
|+++++++++++++++++++++++++++++++++++++ | 73% ~00s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 5
| | 0 % ~calculating
|+++++ | 9 % ~00s
|++++++++++ | 18% ~00s
|++++++++++++++ | 27% ~00s
|+++++++++++++++++++ | 36% ~00s
|+++++++++++++++++++++++ | 45% ~00s
|++++++++++++++++++++++++++++ | 55% ~00s
|++++++++++++++++++++++++++++++++ | 64% ~00s
|+++++++++++++++++++++++++++++++++++++ | 73% ~00s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 6
| | 0 % ~calculating
|+++++ | 9 % ~00s
|++++++++++ | 18% ~00s
|++++++++++++++ | 27% ~00s
|+++++++++++++++++++ | 36% ~00s
|+++++++++++++++++++++++ | 45% ~00s
|++++++++++++++++++++++++++++ | 55% ~00s
|++++++++++++++++++++++++++++++++ | 64% ~00s
|+++++++++++++++++++++++++++++++++++++ | 73% ~00s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 7
| | 0 % ~calculating
|+++++ | 8 % ~00s
|+++++++++ | 17% ~00s
|+++++++++++++ | 25% ~00s
|+++++++++++++++++ | 33% ~00s
|+++++++++++++++++++++ | 42% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++ | 58% ~00s
|++++++++++++++++++++++++++++++++++ | 67% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 8
| | 0 % ~calculating
|++++++ | 11% ~00s
|++++++++++++ | 22% ~00s
|+++++++++++++++++ | 33% ~00s
|+++++++++++++++++++++++ | 44% ~00s
|++++++++++++++++++++++++++++ | 56% ~00s
|++++++++++++++++++++++++++++++++++ | 67% ~00s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 9
| | 0 % ~calculating
|+++++ | 8 % ~00s
|+++++++++ | 17% ~00s
|+++++++++++++ | 25% ~00s
|+++++++++++++++++ | 33% ~00s
|+++++++++++++++++++++ | 42% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++ | 58% ~00s
|++++++++++++++++++++++++++++++++++ | 67% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 10
| | 0 % ~calculating
|++++ | 7 % ~00s
|++++++++ | 14% ~00s
|+++++++++++ | 21% ~00s
|+++++++++++++++ | 29% ~00s
|++++++++++++++++++ | 36% ~00s
|++++++++++++++++++++++ | 43% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|+++++++++++++++++++++++++++++ | 57% ~00s
|+++++++++++++++++++++++++++++++++ | 64% ~00s
|++++++++++++++++++++++++++++++++++++ | 71% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~00s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 11
| | 0 % ~calculating
|++++++ | 11% ~00s
|++++++++++++ | 22% ~00s
|+++++++++++++++++ | 33% ~00s
|+++++++++++++++++++++++ | 44% ~00s
|++++++++++++++++++++++++++++ | 56% ~00s
|++++++++++++++++++++++++++++++++++ | 67% ~00s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
marrangeGrob(plots, nrow=2, ncol=2)
ml <- marrangeGrob(plots, nrow=2, ncol=2)
ggsave(filename = 'immature_sequential_clustering_umaps.pdf', path = file.path(output_dir_plot, '20220721_1'), ml)
Saving 7.29 x 4.5 in image
get_broad_type <- function(celltype){
Excitatory = c('Layer 2 Excitatory Neurons',
'Excitatory CXCL12+ Neurons',
'Cajal Retzius Cells',
'Immature CALB2+ CGE interneuron')
Mature_inhib = c('Mature, SST+ MGE INs',
"Mature, VIP+ CGE INs")
other = c('TBR1+/LRP8+ MS to TC')
immature_neurons = c(
'PROX1 and NKX2.1 Immature INs',
'MAF1+/TSHZ1+ Immature INs',
'CXCR4+ Posterior, MS to TC INs',
'EMX1+/NKX2.1+ Anterior, MS to CC INs',
'EMX1+/LHX6+ Posterior, MS to CC INs',
'GSX2+/RELN+/PAX6+ Clump')
if(celltype %in% Excitatory){
return("EN")
}else if(celltype %in% Mature_inhib){
return('Mature IN')
} else if(celltype %in% immature_neurons){
return('Immature IN')
} else{return('other')}
}
# get the scaled data
scaled_values = as.matrix(GetAssayData(jy_all, slot = "data"))
genes_to_remove = c('DCX', 'DCDC2', 'KIA0319', 'NCAM1')
#cluster_ordering = sort(as.character(Idents(jy_all)), index.return = TRUE)
scaled_values = scaled_values[!rownames(scaled_values)%in% genes_to_remove,]
### Apply separate clustering, need to look into why
hr <- hclust(as.dist(1-cor(t(scaled_values), method="pearson")), method = "complete")
#hc <- hclust(as.dist(1-cor(scaled_values, method="spearman")), method="complete")
broad_labels = unlist(lapply(1:ncol(scaled_values), function(i){
get_broad_type(as.character(Idents(jy_all))[i])
}))
#broad_order = sort(broad_labels, index.return = TRUE)
#final_ox = order( cluster_ordering$x, broad_order$x)
#final_ox = broad_order$ix
#gaps = c()
#for(i in 2:length(broad_order$x)){
# if(broad_order$x[i] != broad_order$x[i-1]){
# gaps = c(gaps, i)
# print(i)
# }
#}
broad_area_sorted = sort(jy_all$area, index.return = TRUE)
final_ox = broad_area_sorted$ix
gaps = c()
for(i in 2:length(broad_area_sorted$x)){
if(broad_area_sorted$x[i] != broad_area_sorted$x[i-1]){
gaps = c(gaps, i)
print(i)
}
}
[1] 250
[1] 431
[1] 699
[1] 803
[1] 974
[1] 1282
[1] 1536
[1] 1626
### Now arrange everything
annotation <- data.frame(cell_type = as.character(Idents(jy_all)),
#cell_class = broad_labels,
area = jy_all$area)
rownames(annotation) <- colnames(scaled_values) # check out the row names of annotation
scaled_values[, final_ox] %>%
pheatmap(annotation_col = annotation,
color = colorRampPalette(brewer.pal(n = 9, name = "Purples"))(100),
#breaks = seq(from= -5, to = 5, by = 11/100.),
cluster_rows = hr, #cluster_cols = hc,
#cluster_rows = TRUE,
cluster_cols = FALSE,
#clustering_distance_rows = "correlation",
#clustering_distance_cols = "correlation",
fontsize_row = 8, fontsize_col = 4, show_colnames = FALSE, gaps_col = gaps,
show_rownames = TRUE, cell_width = 10, cellheight = 8, width = 10, height = 14)#,
# filename = file.path(file.path(output_dir_plot, '20220721_1'), 'ncol_20_sort_by_broad_type_gaps.png'))
plot_features_umap <- function(sobj, gene, pt.size = 3, alpha = 0.8, space = "umap", color = '#CB2A55', flipped = FALSE)
{
coordinates <- Embeddings(sobj, reduction = space)
expmat <- as.matrix(FetchData(sobj, gene))
gene_df <- as.data.frame(cbind(coordinates, expmat))
colnames(gene_df) <- c('X', 'Y', 'expr')
gene_df <- gene_df %>% dplyr::arrange(!is.na(expr), expr)
colors = c('grey90', 'grey90', color)
gene_df$expr[gene_df$expr == 0] = NA
plot <- ggplot(gene_df, aes(x = X, y = Y, color = expr)) + geom_point(size = pt.size, alpha = alpha)+
theme_classic() + ggtitle(gene) + scale_color_gradient(na.value = colors[1], low = colors[2], high = colors[3], labels = NULL) + theme(title = element_text(face = 'bold', size = rel(1), hjust = 1))
if(flipped){
plot <- plot + scale_x_reverse() + scale_y_reverse()
}
return(plot + NoAxes() + NoLegend())
}
genes = rownames(jy_immature)
plots <- lapply(1:length(genes), function(i){
plot_features_umap(jy_immature, genes[i], pt.size = 0.5)
})
umaps = plot_grid(plotlist = plots, label_size = 10, nrow = 4)
umaps
ggsave(plot = umaps, filename = 'test_immature_umapl_expr_plots_size1_alpha1.png', path = file.path(output_dir_plot, '20220721_1'), width = 10, height = 4, dpi = 150)
big_dimplot <- function(sobj, grep_pattern){
dp = DimPlot(sobj, cells.highlight = list(imp = which(grepl(grep_pattern, sobj$area))))
dp <- dp + scale_color_manual(values = c('grey90', '#1982c4'), labels=c('other', grep_pattern)) + NoAxes()
}
patterns = c('408_CC', '408_MS_CC', 'vMS_TC', 'dMS_TC', '408_TC', '164_CC', '164_MS_CC', '164_MS_TC', '164_TC')
plots <- lapply(1:length(patterns), function(i){
big_dimplot(jy_immature, grep_pattern = patterns[i])
})
Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.
Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.
Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.
Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.
Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.
Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.
Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.
Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.
Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.
areas = plot_grid(plotlist = plots, label_size = 10, nrow = 3)
areas
ggsave(plot = areas, filename = 'test_immature_area_plots_size1_alpha1.png', path = file.path(output_dir_plot, '20220721_1'), width = 14, height = 8, dpi = 150)
jy_immature$seurat_clusters = jy_immature$RNA_snn_res.1.1
Idents(jy_immature) = jy_immature$seurat_clusters
DimPlot(jy_immature)
genes = rownames(markers2)
plots <- lapply(1:length(genes), function(i){
plot_features_umap(jy_immature, genes[i], pt.size = 0.5)
})
umaps = plot_grid(plotlist = plots, label_size = 10, nrow = 1)
umaps
ggsave(plot = umaps, filename = 'cluster4_markers_immature_umapl_expr_plots_size1_alpha1.png', path = file.path(output_dir_plot, '20220721_1'), width = 4, height = 1, dpi = 150)
jy_immature.markers <- FindMarkers(jy_immature, ident.1 = 3, ident.2 = 4, only.pos = FALSE)
| | 0 % ~calculating
|++ | 4 % ~00s
|++++ | 7 % ~00s
|++++++ | 11% ~00s
|++++++++ | 14% ~00s
|+++++++++ | 18% ~00s
|+++++++++++ | 21% ~00s
|+++++++++++++ | 25% ~00s
|+++++++++++++++ | 29% ~00s
|+++++++++++++++++ | 32% ~00s
|++++++++++++++++++ | 36% ~00s
|++++++++++++++++++++ | 39% ~00s
|++++++++++++++++++++++ | 43% ~00s
|++++++++++++++++++++++++ | 46% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|+++++++++++++++++++++++++++ | 54% ~00s
|+++++++++++++++++++++++++++++ | 57% ~00s
|+++++++++++++++++++++++++++++++ | 61% ~00s
|+++++++++++++++++++++++++++++++++ | 64% ~00s
|++++++++++++++++++++++++++++++++++ | 68% ~00s
|++++++++++++++++++++++++++++++++++++ | 71% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~00s
|++++++++++++++++++++++++++++++++++++++++++ | 82% ~00s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
markers2 = jy_immature.markers %>%
slice_max(n = 8, order_by = avg_log2FC)
markers2
clusters = as.numeric(sort(unique(jy_408$unified_clusters)))
plots <- lapply(1:length(clusters), function(i){
plot_clusters_vertical_spatial(jy_408, cluster = clusters[i], pt.size = 1, clustering = 'unified_clusters', anterior = FALSE)
})
Error in plot_clusters_vertical_spatial(jy_408, cluster = clusters[i], :
could not find function "plot_clusters_vertical_spatial"
plot_clusters_vertical_spatial <- function(sobj, cluster, clustering = NULL, anterior = FALSE, cluster_color = '#CB2A55', pt.size = 1, space = "H", arc = TRUE)
{
cluster_identity = as.numeric(unlist(ifelse(is.null(clustering),
Idents(sobj) == levels(Idents(sobj))[cluster],sobj[[clustering]]))) == (cluster)
coordinates <- Embeddings(sobj, reduction = space)
gene_df <- as.data.frame(cbind(coordinates, cluster_identity))
colnames(gene_df) <- c('X', 'Y', 'clust')
gene_df <- gene_df %>% dplyr::arrange(clust)
plot <- ggplot(gene_df, aes(x = X, y = Y, color = factor(clust))) + geom_point(size = pt.size, alpha = 1) +
theme_classic() + ggtitle(cluster) + NoAxes() + NoLegend() +
coord_fixed(ratio = 0.5) + theme(title = element_text(face = 'bold', size = rel(0.8), hjust = 1))
cluster_color = scales::hue_pal()(nrow(unique(sobj[[clustering]])))[cluster]
plot = plot + scale_colour_manual(values = c('grey90', cluster_color))
intercept = ifelse(anterior, 660, 484)
if(arc){plot = plot + geom_hline(yintercept=484, linetype = "dashed",color = cluster_color)}
return(plot)
}
plot_clusters_vertical_spatial(jy_164, pt.size = 1, cluster = 3, clustering = 'RNA_snn_res.0.8')
plot_features_vertical_spatial_smoothed <- function(sobj, gene, pt.size = 0.5, space = "H", arc = TRUE)
{
coordinates <- Embeddings(sobj, reduction = space)
expmat <- as.matrix(FetchData(sobj, gene))
gene_df <- as.data.frame(cbind(coordinates, expmat))
colnames(gene_df) <- c('X', 'Y', 'expr')
gene_df <- gene_df %>% dplyr::arrange(!is.na(expr), expr)
colors = c('grey90', 'grey90', '#0f4c5c')
gene_df$expr[gene_df$expr == 0] = NA
plot <- gene_df %>%
filter(!is.na(expr)) %>%
ggplot(aes(x = X, y = Y, color = expr)) +stat_density_2d(aes(fill = ..density..), geom = "raster", n = 400,contour = FALSE, interpolate = TRUE) +
scale_x_continuous(expand = c(0, 0)) +
scale_y_continuous(expand = c(0, 0)) +
#geom_bin2d(bins = 10) + #geom_point(size = pt.size, alpha = 1)+
theme_classic() + ggtitle(gene) + NoAxes() + NoLegend() +
coord_fixed(ratio = 0.5) + scale_fill_gradient(na.value = colors[1], low = colors[2], high = colors[3], labels = NULL) + theme(title = element_text(face = 'bold', size = rel(0.5), hjust = 1))
if(arc){plot = plot + geom_hline(yintercept=660, linetype = "dashed",color = colors[3])}
return(plot)
}
plot_features_vertical_spatial_smoothed(jy_164, gene = 'PAX6')
genes = rownames(jy_408)
plots <- lapply(1:length(genes), function(i){
plot_features_vertical_spatial_smoothed(jy_408, genes[i], pt.size = 1)
})
verts= plot_grid(plotlist = plots, label_size = 10, nrow = 1)
verts
ggsave(plot = verts, filename = 'test_408_smoothed-vertical_expr_plots_size1_alpha1.png', path = file.path(output_dir_plot, '20220721_1'), width = 18, height = 8, dpi = 150)
normed_data <- GetAssayData(jy_all, slot = 'data')
gad_1_true = normed_data['GAD1', ] != 0
jy_all$gad1 = gad_1_true
jy_gad = jy_all[, jy_all$gad1]
breakpoints = 1:20/10+0.3
plots = list()
jy_gad <- FindVariableFeatures(jy_gad, selection.method = "vst")
Calculating gene variances
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating feature variances of standardized and clipped values
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
all.genes <- rownames(jy_gad)
jy_gad <- ScaleData(jy_gad, features = all.genes)
Centering and scaling data matrix
|
| | 0%
|
|=====================================================================================================================================================================| 100%
jy_gad <- FindNeighbors(jy_gad, dims = 1:30)
Computing nearest neighbor graph
Computing SNN
i = 1
for (breakpoint in breakpoints){
jy_gad <- FindClusters(jy_gad, resolution = breakpoint)
jy_gad <- RunUMAP(jy_gad, dims = 1:30)
jy_gad.markers <- FindAllMarkers(jy_gad, only.pos = TRUE, min.pct = 0.25, logfc.threshold = 0.25)
labels = jy_gad.markers %>%
group_by(cluster) %>%
slice_max(n = 1, order_by = avg_log2FC)
new.cluster.ids <- labels$gene
names(new.cluster.ids) <- levels(jy_gad)
jy_gad <- RenameIdents(jy_gad, new.cluster.ids)
plots[[i]] = DimPlot(jy_gad, reduction = "umap", pt.size = 1, label = TRUE) + NoAxes() + NoLegend() + ggtitle(breakpoint)
i = i + 1
}
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 1137
Number of edges: 39738
Running Louvain algorithm...
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Maximum modularity in 10 random starts: 0.7572
Number of communities: 5
Elapsed time: 0 seconds
13:03:06 UMAP embedding parameters a = 0.9922 b = 1.112
13:03:06 Read 1137 rows and found 30 numeric columns
13:03:06 Using Annoy for neighbor search, n_neighbors = 30
13:03:06 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
13:03:06 Writing NN index file to temp file /tmp/RtmpfJenoj/file767657dabb5f1
13:03:06 Searching Annoy index using 1 thread, search_k = 3000
13:03:06 Annoy recall = 100%
13:03:06 Commencing smooth kNN distance calibration using 1 thread
13:03:07 Initializing from normalized Laplacian + noise
13:03:07 Commencing optimization for 500 epochs, with 43072 positive edges
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
13:03:08 Optimization finished
Calculating cluster 0
| | 0 % ~calculating
|+++ | 6 % ~00s
|++++++ | 11% ~00s
|+++++++++ | 17% ~00s
|++++++++++++ | 22% ~00s
|++++++++++++++ | 28% ~00s
|+++++++++++++++++ | 33% ~00s
|++++++++++++++++++++ | 39% ~00s
|+++++++++++++++++++++++ | 44% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++ | 56% ~00s
|+++++++++++++++++++++++++++++++ | 61% ~00s
|++++++++++++++++++++++++++++++++++ | 67% ~00s
|+++++++++++++++++++++++++++++++++++++ | 72% ~00s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~00s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 1
| | 0 % ~calculating
|+++++ | 9 % ~00s
|++++++++++ | 18% ~00s
|++++++++++++++ | 27% ~00s
|+++++++++++++++++++ | 36% ~00s
|+++++++++++++++++++++++ | 45% ~00s
|++++++++++++++++++++++++++++ | 55% ~00s
|++++++++++++++++++++++++++++++++ | 64% ~00s
|+++++++++++++++++++++++++++++++++++++ | 73% ~00s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 2
| | 0 % ~calculating
|++++++ | 11% ~00s
|++++++++++++ | 22% ~00s
|+++++++++++++++++ | 33% ~00s
|+++++++++++++++++++++++ | 44% ~00s
|++++++++++++++++++++++++++++ | 56% ~00s
|++++++++++++++++++++++++++++++++++ | 67% ~00s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 3
| | 0 % ~calculating
|+++++ | 9 % ~00s
|++++++++++ | 18% ~00s
|++++++++++++++ | 27% ~00s
|+++++++++++++++++++ | 36% ~00s
|+++++++++++++++++++++++ | 45% ~00s
|++++++++++++++++++++++++++++ | 55% ~00s
|++++++++++++++++++++++++++++++++ | 64% ~00s
|+++++++++++++++++++++++++++++++++++++ | 73% ~00s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 4
| | 0 % ~calculating
|+++++ | 9 % ~00s
|++++++++++ | 18% ~00s
|++++++++++++++ | 27% ~00s
|+++++++++++++++++++ | 36% ~00s
|+++++++++++++++++++++++ | 45% ~00s
|++++++++++++++++++++++++++++ | 55% ~00s
|++++++++++++++++++++++++++++++++ | 64% ~00s
|+++++++++++++++++++++++++++++++++++++ | 73% ~00s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 1137
Number of edges: 39738
Running Louvain algorithm...
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Maximum modularity in 10 random starts: 0.7338
Number of communities: 6
Elapsed time: 0 seconds
13:03:09 UMAP embedding parameters a = 0.9922 b = 1.112
13:03:09 Read 1137 rows and found 30 numeric columns
13:03:09 Using Annoy for neighbor search, n_neighbors = 30
13:03:09 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
13:03:09 Writing NN index file to temp file /tmp/RtmpfJenoj/file76765529e9362
13:03:09 Searching Annoy index using 1 thread, search_k = 3000
13:03:09 Annoy recall = 100%
13:03:10 Commencing smooth kNN distance calibration using 1 thread
13:03:10 Initializing from normalized Laplacian + noise
13:03:10 Commencing optimization for 500 epochs, with 43072 positive edges
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
13:03:12 Optimization finished
Calculating cluster 0
| | 0 % ~calculating
|+++++ | 9 % ~00s
|++++++++++ | 18% ~00s
|++++++++++++++ | 27% ~00s
|+++++++++++++++++++ | 36% ~00s
|+++++++++++++++++++++++ | 45% ~00s
|++++++++++++++++++++++++++++ | 55% ~00s
|++++++++++++++++++++++++++++++++ | 64% ~00s
|+++++++++++++++++++++++++++++++++++++ | 73% ~00s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 1
| | 0 % ~calculating
|+++++ | 9 % ~00s
|++++++++++ | 18% ~00s
|++++++++++++++ | 27% ~00s
|+++++++++++++++++++ | 36% ~00s
|+++++++++++++++++++++++ | 45% ~00s
|++++++++++++++++++++++++++++ | 55% ~00s
|++++++++++++++++++++++++++++++++ | 64% ~00s
|+++++++++++++++++++++++++++++++++++++ | 73% ~00s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 2
| | 0 % ~calculating
|++++ | 7 % ~00s
|+++++++ | 13% ~00s
|++++++++++ | 20% ~00s
|++++++++++++++ | 27% ~00s
|+++++++++++++++++ | 33% ~00s
|++++++++++++++++++++ | 40% ~00s
|++++++++++++++++++++++++ | 47% ~00s
|+++++++++++++++++++++++++++ | 53% ~00s
|++++++++++++++++++++++++++++++ | 60% ~00s
|++++++++++++++++++++++++++++++++++ | 67% ~00s
|+++++++++++++++++++++++++++++++++++++ | 73% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 3
| | 0 % ~calculating
|+++++ | 8 % ~00s
|+++++++++ | 17% ~00s
|+++++++++++++ | 25% ~00s
|+++++++++++++++++ | 33% ~00s
|+++++++++++++++++++++ | 42% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++ | 58% ~00s
|++++++++++++++++++++++++++++++++++ | 67% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 4
| | 0 % ~calculating
|+++++++ | 12% ~00s
|+++++++++++++ | 25% ~00s
|+++++++++++++++++++ | 38% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++++ | 62% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 5
| | 0 % ~calculating
|+++++ | 8 % ~00s
|+++++++++ | 17% ~00s
|+++++++++++++ | 25% ~00s
|+++++++++++++++++ | 33% ~00s
|+++++++++++++++++++++ | 42% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++ | 58% ~00s
|++++++++++++++++++++++++++++++++++ | 67% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 1137
Number of edges: 39738
Running Louvain algorithm...
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Maximum modularity in 10 random starts: 0.7155
Number of communities: 7
Elapsed time: 0 seconds
13:03:12 UMAP embedding parameters a = 0.9922 b = 1.112
13:03:12 Read 1137 rows and found 30 numeric columns
13:03:12 Using Annoy for neighbor search, n_neighbors = 30
13:03:12 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
13:03:12 Writing NN index file to temp file /tmp/RtmpfJenoj/file767656f6088aa
13:03:12 Searching Annoy index using 1 thread, search_k = 3000
13:03:13 Annoy recall = 100%
13:03:13 Commencing smooth kNN distance calibration using 1 thread
13:03:14 Initializing from normalized Laplacian + noise
13:03:14 Commencing optimization for 500 epochs, with 43072 positive edges
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
13:03:15 Optimization finished
Calculating cluster 0
| | 0 % ~calculating
|+++++ | 9 % ~00s
|++++++++++ | 18% ~00s
|++++++++++++++ | 27% ~00s
|+++++++++++++++++++ | 36% ~00s
|+++++++++++++++++++++++ | 45% ~00s
|++++++++++++++++++++++++++++ | 55% ~00s
|++++++++++++++++++++++++++++++++ | 64% ~00s
|+++++++++++++++++++++++++++++++++++++ | 73% ~00s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 1
| | 0 % ~calculating
|+++++ | 9 % ~00s
|++++++++++ | 18% ~00s
|++++++++++++++ | 27% ~00s
|+++++++++++++++++++ | 36% ~00s
|+++++++++++++++++++++++ | 45% ~00s
|++++++++++++++++++++++++++++ | 55% ~00s
|++++++++++++++++++++++++++++++++ | 64% ~00s
|+++++++++++++++++++++++++++++++++++++ | 73% ~00s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 2
| | 0 % ~calculating
|+++++ | 10% ~00s
|++++++++++ | 20% ~00s
|+++++++++++++++ | 30% ~00s
|++++++++++++++++++++ | 40% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++ | 60% ~00s
|+++++++++++++++++++++++++++++++++++ | 70% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 3
| | 0 % ~calculating
|+++++ | 8 % ~00s
|+++++++++ | 17% ~00s
|+++++++++++++ | 25% ~00s
|+++++++++++++++++ | 33% ~00s
|+++++++++++++++++++++ | 42% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++ | 58% ~00s
|++++++++++++++++++++++++++++++++++ | 67% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 4
| | 0 % ~calculating
|+++++++ | 12% ~00s
|+++++++++++++ | 25% ~00s
|+++++++++++++++++++ | 38% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++++ | 62% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 5
| | 0 % ~calculating
|+++++ | 10% ~00s
|++++++++++ | 20% ~00s
|+++++++++++++++ | 30% ~00s
|++++++++++++++++++++ | 40% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++ | 60% ~00s
|+++++++++++++++++++++++++++++++++++ | 70% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 6
| | 0 % ~calculating
|+++++ | 8 % ~00s
|+++++++++ | 17% ~00s
|+++++++++++++ | 25% ~00s
|+++++++++++++++++ | 33% ~00s
|+++++++++++++++++++++ | 42% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++ | 58% ~00s
|++++++++++++++++++++++++++++++++++ | 67% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 1137
Number of edges: 39738
Running Louvain algorithm...
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Maximum modularity in 10 random starts: 0.6971
Number of communities: 7
Elapsed time: 0 seconds
13:03:16 UMAP embedding parameters a = 0.9922 b = 1.112
13:03:16 Read 1137 rows and found 30 numeric columns
13:03:16 Using Annoy for neighbor search, n_neighbors = 30
13:03:16 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
13:03:16 Writing NN index file to temp file /tmp/RtmpfJenoj/file76765cdbea8b
13:03:16 Searching Annoy index using 1 thread, search_k = 3000
13:03:16 Annoy recall = 100%
13:03:17 Commencing smooth kNN distance calibration using 1 thread
13:03:17 Initializing from normalized Laplacian + noise
13:03:17 Commencing optimization for 500 epochs, with 43072 positive edges
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
13:03:19 Optimization finished
Calculating cluster 0
| | 0 % ~calculating
|+++++ | 9 % ~00s
|++++++++++ | 18% ~00s
|++++++++++++++ | 27% ~00s
|+++++++++++++++++++ | 36% ~00s
|+++++++++++++++++++++++ | 45% ~00s
|++++++++++++++++++++++++++++ | 55% ~00s
|++++++++++++++++++++++++++++++++ | 64% ~00s
|+++++++++++++++++++++++++++++++++++++ | 73% ~00s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 1
| | 0 % ~calculating
|+++++ | 9 % ~00s
|++++++++++ | 18% ~00s
|++++++++++++++ | 27% ~00s
|+++++++++++++++++++ | 36% ~00s
|+++++++++++++++++++++++ | 45% ~00s
|++++++++++++++++++++++++++++ | 55% ~00s
|++++++++++++++++++++++++++++++++ | 64% ~00s
|+++++++++++++++++++++++++++++++++++++ | 73% ~00s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 2
| | 0 % ~calculating
|+++++ | 10% ~00s
|++++++++++ | 20% ~00s
|+++++++++++++++ | 30% ~00s
|++++++++++++++++++++ | 40% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++ | 60% ~00s
|+++++++++++++++++++++++++++++++++++ | 70% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 3
| | 0 % ~calculating
|+++++ | 9 % ~00s
|++++++++++ | 18% ~00s
|++++++++++++++ | 27% ~00s
|+++++++++++++++++++ | 36% ~00s
|+++++++++++++++++++++++ | 45% ~00s
|++++++++++++++++++++++++++++ | 55% ~00s
|++++++++++++++++++++++++++++++++ | 64% ~00s
|+++++++++++++++++++++++++++++++++++++ | 73% ~00s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 4
| | 0 % ~calculating
|+++++++ | 12% ~00s
|+++++++++++++ | 25% ~00s
|+++++++++++++++++++ | 38% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++++ | 62% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 5
| | 0 % ~calculating
|+++++ | 10% ~00s
|++++++++++ | 20% ~00s
|+++++++++++++++ | 30% ~00s
|++++++++++++++++++++ | 40% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++ | 60% ~00s
|+++++++++++++++++++++++++++++++++++ | 70% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 6
| | 0 % ~calculating
|+++++ | 8 % ~00s
|+++++++++ | 17% ~00s
|+++++++++++++ | 25% ~00s
|+++++++++++++++++ | 33% ~00s
|+++++++++++++++++++++ | 42% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++ | 58% ~00s
|++++++++++++++++++++++++++++++++++ | 67% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 1137
Number of edges: 39738
Running Louvain algorithm...
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Maximum modularity in 10 random starts: 0.6796
Number of communities: 8
Elapsed time: 0 seconds
13:03:19 UMAP embedding parameters a = 0.9922 b = 1.112
13:03:19 Read 1137 rows and found 30 numeric columns
13:03:19 Using Annoy for neighbor search, n_neighbors = 30
13:03:19 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
13:03:19 Writing NN index file to temp file /tmp/RtmpfJenoj/file7676569b8b375
13:03:19 Searching Annoy index using 1 thread, search_k = 3000
13:03:20 Annoy recall = 100%
13:03:20 Commencing smooth kNN distance calibration using 1 thread
13:03:21 Initializing from normalized Laplacian + noise
13:03:21 Commencing optimization for 500 epochs, with 43072 positive edges
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
13:03:22 Optimization finished
Calculating cluster 0
| | 0 % ~calculating
|+++++ | 10% ~00s
|++++++++++ | 20% ~00s
|+++++++++++++++ | 30% ~00s
|++++++++++++++++++++ | 40% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++ | 60% ~00s
|+++++++++++++++++++++++++++++++++++ | 70% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 1
| | 0 % ~calculating
|+++++ | 9 % ~00s
|++++++++++ | 18% ~00s
|++++++++++++++ | 27% ~00s
|+++++++++++++++++++ | 36% ~00s
|+++++++++++++++++++++++ | 45% ~00s
|++++++++++++++++++++++++++++ | 55% ~00s
|++++++++++++++++++++++++++++++++ | 64% ~00s
|+++++++++++++++++++++++++++++++++++++ | 73% ~00s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 2
| | 0 % ~calculating
|+++++ | 10% ~00s
|++++++++++ | 20% ~00s
|+++++++++++++++ | 30% ~00s
|++++++++++++++++++++ | 40% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++ | 60% ~00s
|+++++++++++++++++++++++++++++++++++ | 70% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 3
| | 0 % ~calculating
|+++++ | 10% ~00s
|++++++++++ | 20% ~00s
|+++++++++++++++ | 30% ~00s
|++++++++++++++++++++ | 40% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++ | 60% ~00s
|+++++++++++++++++++++++++++++++++++ | 70% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 4
| | 0 % ~calculating
|+++++++ | 12% ~00s
|+++++++++++++ | 25% ~00s
|+++++++++++++++++++ | 38% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++++ | 62% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 5
| | 0 % ~calculating
|+++++++ | 12% ~00s
|+++++++++++++ | 25% ~00s
|+++++++++++++++++++ | 38% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++++ | 62% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 6
| | 0 % ~calculating
|+++++ | 10% ~00s
|++++++++++ | 20% ~00s
|+++++++++++++++ | 30% ~00s
|++++++++++++++++++++ | 40% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++ | 60% ~00s
|+++++++++++++++++++++++++++++++++++ | 70% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 7
| | 0 % ~calculating
|+++++ | 8 % ~00s
|+++++++++ | 17% ~00s
|+++++++++++++ | 25% ~00s
|+++++++++++++++++ | 33% ~00s
|+++++++++++++++++++++ | 42% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++ | 58% ~00s
|++++++++++++++++++++++++++++++++++ | 67% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 1137
Number of edges: 39738
Running Louvain algorithm...
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Maximum modularity in 10 random starts: 0.6643
Number of communities: 9
Elapsed time: 0 seconds
13:03:23 UMAP embedding parameters a = 0.9922 b = 1.112
13:03:23 Read 1137 rows and found 30 numeric columns
13:03:23 Using Annoy for neighbor search, n_neighbors = 30
13:03:23 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
13:03:23 Writing NN index file to temp file /tmp/RtmpfJenoj/file767657dc91999
13:03:23 Searching Annoy index using 1 thread, search_k = 3000
13:03:23 Annoy recall = 100%
13:03:24 Commencing smooth kNN distance calibration using 1 thread
13:03:24 Initializing from normalized Laplacian + noise
13:03:24 Commencing optimization for 500 epochs, with 43072 positive edges
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
13:03:26 Optimization finished
Calculating cluster 0
| | 0 % ~calculating
|+++++ | 10% ~00s
|++++++++++ | 20% ~00s
|+++++++++++++++ | 30% ~00s
|++++++++++++++++++++ | 40% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++ | 60% ~00s
|+++++++++++++++++++++++++++++++++++ | 70% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 1
| | 0 % ~calculating
|+++++ | 9 % ~00s
|++++++++++ | 18% ~00s
|++++++++++++++ | 27% ~00s
|+++++++++++++++++++ | 36% ~00s
|+++++++++++++++++++++++ | 45% ~00s
|++++++++++++++++++++++++++++ | 55% ~00s
|++++++++++++++++++++++++++++++++ | 64% ~00s
|+++++++++++++++++++++++++++++++++++++ | 73% ~00s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 2
| | 0 % ~calculating
|+++++ | 10% ~00s
|++++++++++ | 20% ~00s
|+++++++++++++++ | 30% ~00s
|++++++++++++++++++++ | 40% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++ | 60% ~00s
|+++++++++++++++++++++++++++++++++++ | 70% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 3
| | 0 % ~calculating
|+++++ | 10% ~00s
|++++++++++ | 20% ~00s
|+++++++++++++++ | 30% ~00s
|++++++++++++++++++++ | 40% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++ | 60% ~00s
|+++++++++++++++++++++++++++++++++++ | 70% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 4
| | 0 % ~calculating
|+++++++ | 12% ~00s
|+++++++++++++ | 25% ~00s
|+++++++++++++++++++ | 38% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++++ | 62% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 5
| | 0 % ~calculating
|+++++++ | 12% ~00s
|+++++++++++++ | 25% ~00s
|+++++++++++++++++++ | 38% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++++ | 62% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 6
| | 0 % ~calculating
|+++++ | 10% ~00s
|++++++++++ | 20% ~00s
|+++++++++++++++ | 30% ~00s
|++++++++++++++++++++ | 40% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++ | 60% ~00s
|+++++++++++++++++++++++++++++++++++ | 70% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 7
| | 0 % ~calculating
|+++++ | 8 % ~00s
|+++++++++ | 17% ~00s
|+++++++++++++ | 25% ~00s
|+++++++++++++++++ | 33% ~00s
|+++++++++++++++++++++ | 42% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++ | 58% ~00s
|++++++++++++++++++++++++++++++++++ | 67% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 8
| | 0 % ~calculating
|+++++ | 10% ~00s
|++++++++++ | 20% ~00s
|+++++++++++++++ | 30% ~00s
|++++++++++++++++++++ | 40% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++ | 60% ~00s
|+++++++++++++++++++++++++++++++++++ | 70% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 1137
Number of edges: 39738
Running Louvain algorithm...
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Maximum modularity in 10 random starts: 0.6502
Number of communities: 9
Elapsed time: 0 seconds
13:03:26 UMAP embedding parameters a = 0.9922 b = 1.112
13:03:26 Read 1137 rows and found 30 numeric columns
13:03:26 Using Annoy for neighbor search, n_neighbors = 30
13:03:26 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
13:03:26 Writing NN index file to temp file /tmp/RtmpfJenoj/file76765b780f30
13:03:26 Searching Annoy index using 1 thread, search_k = 3000
13:03:27 Annoy recall = 100%
13:03:27 Commencing smooth kNN distance calibration using 1 thread
13:03:28 Initializing from normalized Laplacian + noise
13:03:28 Commencing optimization for 500 epochs, with 43072 positive edges
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
13:03:29 Optimization finished
Calculating cluster 0
| | 0 % ~calculating
|+++++ | 10% ~00s
|++++++++++ | 20% ~00s
|+++++++++++++++ | 30% ~00s
|++++++++++++++++++++ | 40% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++ | 60% ~00s
|+++++++++++++++++++++++++++++++++++ | 70% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 1
| | 0 % ~calculating
|+++++ | 9 % ~00s
|++++++++++ | 18% ~00s
|++++++++++++++ | 27% ~00s
|+++++++++++++++++++ | 36% ~00s
|+++++++++++++++++++++++ | 45% ~00s
|++++++++++++++++++++++++++++ | 55% ~00s
|++++++++++++++++++++++++++++++++ | 64% ~00s
|+++++++++++++++++++++++++++++++++++++ | 73% ~00s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 2
| | 0 % ~calculating
|+++++ | 10% ~00s
|++++++++++ | 20% ~00s
|+++++++++++++++ | 30% ~00s
|++++++++++++++++++++ | 40% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++ | 60% ~00s
|+++++++++++++++++++++++++++++++++++ | 70% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 3
| | 0 % ~calculating
|+++++ | 10% ~00s
|++++++++++ | 20% ~00s
|+++++++++++++++ | 30% ~00s
|++++++++++++++++++++ | 40% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++ | 60% ~00s
|+++++++++++++++++++++++++++++++++++ | 70% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 4
| | 0 % ~calculating
|+++++++ | 12% ~00s
|+++++++++++++ | 25% ~00s
|+++++++++++++++++++ | 38% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++++ | 62% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 5
| | 0 % ~calculating
|+++++++ | 12% ~00s
|+++++++++++++ | 25% ~00s
|+++++++++++++++++++ | 38% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++++ | 62% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 6
| | 0 % ~calculating
|+++++ | 10% ~00s
|++++++++++ | 20% ~00s
|+++++++++++++++ | 30% ~00s
|++++++++++++++++++++ | 40% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++ | 60% ~00s
|+++++++++++++++++++++++++++++++++++ | 70% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 7
| | 0 % ~calculating
|+++++ | 8 % ~00s
|+++++++++ | 17% ~00s
|+++++++++++++ | 25% ~00s
|+++++++++++++++++ | 33% ~00s
|+++++++++++++++++++++ | 42% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++ | 58% ~00s
|++++++++++++++++++++++++++++++++++ | 67% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 8
| | 0 % ~calculating
|+++++ | 10% ~00s
|++++++++++ | 20% ~00s
|+++++++++++++++ | 30% ~00s
|++++++++++++++++++++ | 40% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++ | 60% ~00s
|+++++++++++++++++++++++++++++++++++ | 70% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 1137
Number of edges: 39738
Running Louvain algorithm...
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Maximum modularity in 10 random starts: 0.6350
Number of communities: 10
Elapsed time: 0 seconds
13:03:30 UMAP embedding parameters a = 0.9922 b = 1.112
13:03:30 Read 1137 rows and found 30 numeric columns
13:03:30 Using Annoy for neighbor search, n_neighbors = 30
13:03:30 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
13:03:30 Writing NN index file to temp file /tmp/RtmpfJenoj/file76765231577fc
13:03:30 Searching Annoy index using 1 thread, search_k = 3000
13:03:30 Annoy recall = 100%
13:03:31 Commencing smooth kNN distance calibration using 1 thread
13:03:32 Initializing from normalized Laplacian + noise
13:03:32 Commencing optimization for 500 epochs, with 43072 positive edges
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
13:03:33 Optimization finished
Calculating cluster 0
| | 0 % ~calculating
|+++++ | 9 % ~00s
|++++++++++ | 18% ~00s
|++++++++++++++ | 27% ~00s
|+++++++++++++++++++ | 36% ~00s
|+++++++++++++++++++++++ | 45% ~00s
|++++++++++++++++++++++++++++ | 55% ~00s
|++++++++++++++++++++++++++++++++ | 64% ~00s
|+++++++++++++++++++++++++++++++++++++ | 73% ~00s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 1
| | 0 % ~calculating
|+++++ | 10% ~00s
|++++++++++ | 20% ~00s
|+++++++++++++++ | 30% ~00s
|++++++++++++++++++++ | 40% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++ | 60% ~00s
|+++++++++++++++++++++++++++++++++++ | 70% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 2
| | 0 % ~calculating
|+++++ | 10% ~00s
|++++++++++ | 20% ~00s
|+++++++++++++++ | 30% ~00s
|++++++++++++++++++++ | 40% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++ | 60% ~00s
|+++++++++++++++++++++++++++++++++++ | 70% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 3
| | 0 % ~calculating
|+++++++ | 12% ~00s
|+++++++++++++ | 25% ~00s
|+++++++++++++++++++ | 38% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++++ | 62% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 4
| | 0 % ~calculating
|+++++++ | 12% ~00s
|+++++++++++++ | 25% ~00s
|+++++++++++++++++++ | 38% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++++ | 62% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 5
| | 0 % ~calculating
|+++++++ | 12% ~00s
|+++++++++++++ | 25% ~00s
|+++++++++++++++++++ | 38% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++++ | 62% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 6
| | 0 % ~calculating
|+++++ | 10% ~00s
|++++++++++ | 20% ~00s
|+++++++++++++++ | 30% ~00s
|++++++++++++++++++++ | 40% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++ | 60% ~00s
|+++++++++++++++++++++++++++++++++++ | 70% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 7
| | 0 % ~calculating
|+++++ | 8 % ~00s
|+++++++++ | 17% ~00s
|+++++++++++++ | 25% ~00s
|+++++++++++++++++ | 33% ~00s
|+++++++++++++++++++++ | 42% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++ | 58% ~00s
|++++++++++++++++++++++++++++++++++ | 67% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 8
| | 0 % ~calculating
|++++ | 6 % ~00s
|+++++++ | 12% ~00s
|++++++++++ | 19% ~00s
|+++++++++++++ | 25% ~00s
|++++++++++++++++ | 31% ~00s
|+++++++++++++++++++ | 38% ~00s
|++++++++++++++++++++++ | 44% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|+++++++++++++++++++++++++++++ | 56% ~00s
|++++++++++++++++++++++++++++++++ | 62% ~00s
|+++++++++++++++++++++++++++++++++++ | 69% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 9
| | 0 % ~calculating
|+++++ | 10% ~00s
|++++++++++ | 20% ~00s
|+++++++++++++++ | 30% ~00s
|++++++++++++++++++++ | 40% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++ | 60% ~00s
|+++++++++++++++++++++++++++++++++++ | 70% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 1137
Number of edges: 39738
Running Louvain algorithm...
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Maximum modularity in 10 random starts: 0.6222
Number of communities: 10
Elapsed time: 0 seconds
13:03:34 UMAP embedding parameters a = 0.9922 b = 1.112
13:03:34 Read 1137 rows and found 30 numeric columns
13:03:34 Using Annoy for neighbor search, n_neighbors = 30
13:03:34 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
13:03:34 Writing NN index file to temp file /tmp/RtmpfJenoj/file7676513ea33a3
13:03:34 Searching Annoy index using 1 thread, search_k = 3000
13:03:34 Annoy recall = 100%
13:03:34 Commencing smooth kNN distance calibration using 1 thread
13:03:35 Initializing from normalized Laplacian + noise
13:03:35 Commencing optimization for 500 epochs, with 43072 positive edges
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
13:03:37 Optimization finished
Calculating cluster 0
| | 0 % ~calculating
|+++++ | 10% ~00s
|++++++++++ | 20% ~00s
|+++++++++++++++ | 30% ~00s
|++++++++++++++++++++ | 40% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++ | 60% ~00s
|+++++++++++++++++++++++++++++++++++ | 70% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 1
| | 0 % ~calculating
|+++++ | 10% ~00s
|++++++++++ | 20% ~00s
|+++++++++++++++ | 30% ~00s
|++++++++++++++++++++ | 40% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++ | 60% ~00s
|+++++++++++++++++++++++++++++++++++ | 70% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 2
| | 0 % ~calculating
|++++++ | 11% ~00s
|++++++++++++ | 22% ~00s
|+++++++++++++++++ | 33% ~00s
|+++++++++++++++++++++++ | 44% ~00s
|++++++++++++++++++++++++++++ | 56% ~00s
|++++++++++++++++++++++++++++++++++ | 67% ~00s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 3
| | 0 % ~calculating
|+++++++ | 12% ~00s
|+++++++++++++ | 25% ~00s
|+++++++++++++++++++ | 38% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++++ | 62% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 4
| | 0 % ~calculating
|++++++++ | 14% ~00s
|+++++++++++++++ | 29% ~00s
|++++++++++++++++++++++ | 43% ~00s
|+++++++++++++++++++++++++++++ | 57% ~00s
|++++++++++++++++++++++++++++++++++++ | 71% ~00s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 5
| | 0 % ~calculating
|+++++++ | 12% ~00s
|+++++++++++++ | 25% ~00s
|+++++++++++++++++++ | 38% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++++ | 62% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 6
| | 0 % ~calculating
|++++ | 8 % ~00s
|++++++++ | 15% ~00s
|++++++++++++ | 23% ~00s
|++++++++++++++++ | 31% ~00s
|++++++++++++++++++++ | 38% ~00s
|++++++++++++++++++++++++ | 46% ~00s
|+++++++++++++++++++++++++++ | 54% ~00s
|+++++++++++++++++++++++++++++++ | 62% ~00s
|+++++++++++++++++++++++++++++++++++ | 69% ~00s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~00s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 7
| | 0 % ~calculating
|+++++ | 10% ~00s
|++++++++++ | 20% ~00s
|+++++++++++++++ | 30% ~00s
|++++++++++++++++++++ | 40% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++ | 60% ~00s
|+++++++++++++++++++++++++++++++++++ | 70% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 8
| | 0 % ~calculating
|+++++ | 8 % ~00s
|+++++++++ | 17% ~00s
|+++++++++++++ | 25% ~00s
|+++++++++++++++++ | 33% ~00s
|+++++++++++++++++++++ | 42% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++ | 58% ~00s
|++++++++++++++++++++++++++++++++++ | 67% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 9
| | 0 % ~calculating
|+++++ | 10% ~00s
|++++++++++ | 20% ~00s
|+++++++++++++++ | 30% ~00s
|++++++++++++++++++++ | 40% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++ | 60% ~00s
|+++++++++++++++++++++++++++++++++++ | 70% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 1137
Number of edges: 39738
Running Louvain algorithm...
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Maximum modularity in 10 random starts: 0.6108
Number of communities: 11
Elapsed time: 0 seconds
13:03:37 UMAP embedding parameters a = 0.9922 b = 1.112
13:03:37 Read 1137 rows and found 30 numeric columns
13:03:37 Using Annoy for neighbor search, n_neighbors = 30
13:03:37 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
13:03:38 Writing NN index file to temp file /tmp/RtmpfJenoj/file76765258e0317
13:03:38 Searching Annoy index using 1 thread, search_k = 3000
13:03:38 Annoy recall = 100%
13:03:38 Commencing smooth kNN distance calibration using 1 thread
13:03:39 Initializing from normalized Laplacian + noise
13:03:39 Commencing optimization for 500 epochs, with 43072 positive edges
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
13:03:40 Optimization finished
Calculating cluster 0
| | 0 % ~calculating
|+++++ | 9 % ~00s
|++++++++++ | 18% ~00s
|++++++++++++++ | 27% ~00s
|+++++++++++++++++++ | 36% ~00s
|+++++++++++++++++++++++ | 45% ~00s
|++++++++++++++++++++++++++++ | 55% ~00s
|++++++++++++++++++++++++++++++++ | 64% ~00s
|+++++++++++++++++++++++++++++++++++++ | 73% ~00s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 1
| | 0 % ~calculating
|+++++ | 10% ~00s
|++++++++++ | 20% ~00s
|+++++++++++++++ | 30% ~00s
|++++++++++++++++++++ | 40% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++ | 60% ~00s
|+++++++++++++++++++++++++++++++++++ | 70% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 2
| | 0 % ~calculating
|++++++ | 11% ~00s
|++++++++++++ | 22% ~00s
|+++++++++++++++++ | 33% ~00s
|+++++++++++++++++++++++ | 44% ~00s
|++++++++++++++++++++++++++++ | 56% ~00s
|++++++++++++++++++++++++++++++++++ | 67% ~00s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 3
| | 0 % ~calculating
|+++++++ | 12% ~00s
|+++++++++++++ | 25% ~00s
|+++++++++++++++++++ | 38% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++++ | 62% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 4
| | 0 % ~calculating
|++++++++ | 14% ~00s
|+++++++++++++++ | 29% ~00s
|++++++++++++++++++++++ | 43% ~00s
|+++++++++++++++++++++++++++++ | 57% ~00s
|++++++++++++++++++++++++++++++++++++ | 71% ~00s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 5
| | 0 % ~calculating
|++++++++ | 14% ~00s
|+++++++++++++++ | 29% ~00s
|++++++++++++++++++++++ | 43% ~00s
|+++++++++++++++++++++++++++++ | 57% ~00s
|++++++++++++++++++++++++++++++++++++ | 71% ~00s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 6
| | 0 % ~calculating
|++++ | 8 % ~00s
|++++++++ | 15% ~00s
|++++++++++++ | 23% ~00s
|++++++++++++++++ | 31% ~00s
|++++++++++++++++++++ | 38% ~00s
|++++++++++++++++++++++++ | 46% ~00s
|+++++++++++++++++++++++++++ | 54% ~00s
|+++++++++++++++++++++++++++++++ | 62% ~00s
|+++++++++++++++++++++++++++++++++++ | 69% ~00s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~00s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 7
| | 0 % ~calculating
|+++++ | 10% ~00s
|++++++++++ | 20% ~00s
|+++++++++++++++ | 30% ~00s
|++++++++++++++++++++ | 40% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++ | 60% ~00s
|+++++++++++++++++++++++++++++++++++ | 70% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 8
| | 0 % ~calculating
|+++++ | 8 % ~00s
|+++++++++ | 17% ~00s
|+++++++++++++ | 25% ~00s
|+++++++++++++++++ | 33% ~00s
|+++++++++++++++++++++ | 42% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++ | 58% ~00s
|++++++++++++++++++++++++++++++++++ | 67% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 9
| | 0 % ~calculating
|+++++ | 10% ~00s
|++++++++++ | 20% ~00s
|+++++++++++++++ | 30% ~00s
|++++++++++++++++++++ | 40% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++ | 60% ~00s
|+++++++++++++++++++++++++++++++++++ | 70% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 10
| | 0 % ~calculating
|+++++++++ | 17% ~00s
|+++++++++++++++++ | 33% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++++++ | 67% ~00s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 1137
Number of edges: 39738
Running Louvain algorithm...
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Maximum modularity in 10 random starts: 0.5994
Number of communities: 11
Elapsed time: 0 seconds
13:03:41 UMAP embedding parameters a = 0.9922 b = 1.112
13:03:41 Read 1137 rows and found 30 numeric columns
13:03:41 Using Annoy for neighbor search, n_neighbors = 30
13:03:41 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
13:03:41 Writing NN index file to temp file /tmp/RtmpfJenoj/file767652cf034bd
13:03:41 Searching Annoy index using 1 thread, search_k = 3000
13:03:42 Annoy recall = 100%
13:03:42 Commencing smooth kNN distance calibration using 1 thread
13:03:43 Initializing from normalized Laplacian + noise
13:03:43 Commencing optimization for 500 epochs, with 43072 positive edges
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
13:03:44 Optimization finished
Calculating cluster 0
| | 0 % ~calculating
|+++++ | 10% ~00s
|++++++++++ | 20% ~00s
|+++++++++++++++ | 30% ~00s
|++++++++++++++++++++ | 40% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++ | 60% ~00s
|+++++++++++++++++++++++++++++++++++ | 70% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 1
| | 0 % ~calculating
|+++++ | 10% ~00s
|++++++++++ | 20% ~00s
|+++++++++++++++ | 30% ~00s
|++++++++++++++++++++ | 40% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++ | 60% ~00s
|+++++++++++++++++++++++++++++++++++ | 70% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 2
| | 0 % ~calculating
|++++++ | 11% ~00s
|++++++++++++ | 22% ~00s
|+++++++++++++++++ | 33% ~00s
|+++++++++++++++++++++++ | 44% ~00s
|++++++++++++++++++++++++++++ | 56% ~00s
|++++++++++++++++++++++++++++++++++ | 67% ~00s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 3
| | 0 % ~calculating
|+++++++ | 12% ~00s
|+++++++++++++ | 25% ~00s
|+++++++++++++++++++ | 38% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++++ | 62% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 4
| | 0 % ~calculating
|+++++++ | 12% ~00s
|+++++++++++++ | 25% ~00s
|+++++++++++++++++++ | 38% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++++ | 62% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 5
| | 0 % ~calculating
|++++++++ | 14% ~00s
|+++++++++++++++ | 29% ~00s
|++++++++++++++++++++++ | 43% ~00s
|+++++++++++++++++++++++++++++ | 57% ~00s
|++++++++++++++++++++++++++++++++++++ | 71% ~00s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 6
| | 0 % ~calculating
|++++ | 8 % ~00s
|++++++++ | 15% ~00s
|++++++++++++ | 23% ~00s
|++++++++++++++++ | 31% ~00s
|++++++++++++++++++++ | 38% ~00s
|++++++++++++++++++++++++ | 46% ~00s
|+++++++++++++++++++++++++++ | 54% ~00s
|+++++++++++++++++++++++++++++++ | 62% ~00s
|+++++++++++++++++++++++++++++++++++ | 69% ~00s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~00s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 7
| | 0 % ~calculating
|+++++ | 10% ~00s
|++++++++++ | 20% ~00s
|+++++++++++++++ | 30% ~00s
|++++++++++++++++++++ | 40% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++ | 60% ~00s
|+++++++++++++++++++++++++++++++++++ | 70% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 8
| | 0 % ~calculating
|+++++ | 10% ~00s
|++++++++++ | 20% ~00s
|+++++++++++++++ | 30% ~00s
|++++++++++++++++++++ | 40% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++ | 60% ~00s
|+++++++++++++++++++++++++++++++++++ | 70% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 9
| | 0 % ~calculating
|+++++ | 8 % ~00s
|+++++++++ | 17% ~00s
|+++++++++++++ | 25% ~00s
|+++++++++++++++++ | 33% ~00s
|+++++++++++++++++++++ | 42% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++ | 58% ~00s
|++++++++++++++++++++++++++++++++++ | 67% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 10
| | 0 % ~calculating
|+++++ | 10% ~00s
|++++++++++ | 20% ~00s
|+++++++++++++++ | 30% ~00s
|++++++++++++++++++++ | 40% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++ | 60% ~00s
|+++++++++++++++++++++++++++++++++++ | 70% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 1137
Number of edges: 39738
Running Louvain algorithm...
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Maximum modularity in 10 random starts: 0.5885
Number of communities: 12
Elapsed time: 0 seconds
13:03:45 UMAP embedding parameters a = 0.9922 b = 1.112
13:03:45 Read 1137 rows and found 30 numeric columns
13:03:45 Using Annoy for neighbor search, n_neighbors = 30
13:03:45 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
13:03:45 Writing NN index file to temp file /tmp/RtmpfJenoj/file7676560fe697
13:03:45 Searching Annoy index using 1 thread, search_k = 3000
13:03:45 Annoy recall = 100%
13:03:46 Commencing smooth kNN distance calibration using 1 thread
13:03:46 Initializing from normalized Laplacian + noise
13:03:46 Commencing optimization for 500 epochs, with 43072 positive edges
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
13:03:48 Optimization finished
Calculating cluster 0
| | 0 % ~calculating
|+++++ | 10% ~00s
|++++++++++ | 20% ~00s
|+++++++++++++++ | 30% ~00s
|++++++++++++++++++++ | 40% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++ | 60% ~00s
|+++++++++++++++++++++++++++++++++++ | 70% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 1
| | 0 % ~calculating
|+++++ | 10% ~00s
|++++++++++ | 20% ~00s
|+++++++++++++++ | 30% ~00s
|++++++++++++++++++++ | 40% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++ | 60% ~00s
|+++++++++++++++++++++++++++++++++++ | 70% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 2
| | 0 % ~calculating
|++++++ | 11% ~00s
|++++++++++++ | 22% ~00s
|+++++++++++++++++ | 33% ~00s
|+++++++++++++++++++++++ | 44% ~00s
|++++++++++++++++++++++++++++ | 56% ~00s
|++++++++++++++++++++++++++++++++++ | 67% ~00s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 3
| | 0 % ~calculating
|+++++++ | 12% ~00s
|+++++++++++++ | 25% ~00s
|+++++++++++++++++++ | 38% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++++ | 62% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 4
| | 0 % ~calculating
|+++++++ | 12% ~00s
|+++++++++++++ | 25% ~00s
|+++++++++++++++++++ | 38% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++++ | 62% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 5
| | 0 % ~calculating
|+++++++ | 12% ~00s
|+++++++++++++ | 25% ~00s
|+++++++++++++++++++ | 38% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++++ | 62% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 6
| | 0 % ~calculating
|++++ | 7 % ~00s
|++++++++ | 14% ~00s
|+++++++++++ | 21% ~00s
|+++++++++++++++ | 29% ~00s
|++++++++++++++++++ | 36% ~00s
|++++++++++++++++++++++ | 43% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|+++++++++++++++++++++++++++++ | 57% ~00s
|+++++++++++++++++++++++++++++++++ | 64% ~00s
|++++++++++++++++++++++++++++++++++++ | 71% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~00s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 7
| | 0 % ~calculating
|+++++ | 9 % ~00s
|++++++++++ | 18% ~00s
|++++++++++++++ | 27% ~00s
|+++++++++++++++++++ | 36% ~00s
|+++++++++++++++++++++++ | 45% ~00s
|++++++++++++++++++++++++++++ | 55% ~00s
|++++++++++++++++++++++++++++++++ | 64% ~00s
|+++++++++++++++++++++++++++++++++++++ | 73% ~00s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 8
| | 0 % ~calculating
|+++++ | 10% ~00s
|++++++++++ | 20% ~00s
|+++++++++++++++ | 30% ~00s
|++++++++++++++++++++ | 40% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++ | 60% ~00s
|+++++++++++++++++++++++++++++++++++ | 70% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 9
| | 0 % ~calculating
|+++++ | 8 % ~00s
|+++++++++ | 17% ~00s
|+++++++++++++ | 25% ~00s
|+++++++++++++++++ | 33% ~00s
|+++++++++++++++++++++ | 42% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++ | 58% ~00s
|++++++++++++++++++++++++++++++++++ | 67% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 10
| | 0 % ~calculating
|+++++ | 10% ~00s
|++++++++++ | 20% ~00s
|+++++++++++++++ | 30% ~00s
|++++++++++++++++++++ | 40% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++ | 60% ~00s
|+++++++++++++++++++++++++++++++++++ | 70% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 11
| | 0 % ~calculating
|+++++++++ | 17% ~00s
|+++++++++++++++++ | 33% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++++++ | 67% ~00s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 1137
Number of edges: 39738
Running Louvain algorithm...
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Maximum modularity in 10 random starts: 0.5778
Number of communities: 12
Elapsed time: 0 seconds
13:03:49 UMAP embedding parameters a = 0.9922 b = 1.112
13:03:49 Read 1137 rows and found 30 numeric columns
13:03:49 Using Annoy for neighbor search, n_neighbors = 30
13:03:49 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
13:03:49 Writing NN index file to temp file /tmp/RtmpfJenoj/file76765173b0aa1
13:03:49 Searching Annoy index using 1 thread, search_k = 3000
13:03:49 Annoy recall = 100%
13:03:50 Commencing smooth kNN distance calibration using 1 thread
13:03:50 Initializing from normalized Laplacian + noise
13:03:50 Commencing optimization for 500 epochs, with 43072 positive edges
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
13:03:52 Optimization finished
Calculating cluster 0
| | 0 % ~calculating
|+++++ | 10% ~00s
|++++++++++ | 20% ~00s
|+++++++++++++++ | 30% ~00s
|++++++++++++++++++++ | 40% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++ | 60% ~00s
|+++++++++++++++++++++++++++++++++++ | 70% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 1
| | 0 % ~calculating
|++++++ | 11% ~00s
|++++++++++++ | 22% ~00s
|+++++++++++++++++ | 33% ~00s
|+++++++++++++++++++++++ | 44% ~00s
|++++++++++++++++++++++++++++ | 56% ~00s
|++++++++++++++++++++++++++++++++++ | 67% ~00s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 2
| | 0 % ~calculating
|+++++ | 10% ~00s
|++++++++++ | 20% ~00s
|+++++++++++++++ | 30% ~00s
|++++++++++++++++++++ | 40% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++ | 60% ~00s
|+++++++++++++++++++++++++++++++++++ | 70% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 3
| | 0 % ~calculating
|+++++++ | 12% ~00s
|+++++++++++++ | 25% ~00s
|+++++++++++++++++++ | 38% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++++ | 62% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 4
| | 0 % ~calculating
|+++++++ | 12% ~00s
|+++++++++++++ | 25% ~00s
|+++++++++++++++++++ | 38% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++++ | 62% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 5
| | 0 % ~calculating
|+++++++ | 12% ~00s
|+++++++++++++ | 25% ~00s
|+++++++++++++++++++ | 38% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++++ | 62% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 6
| | 0 % ~calculating
|++++ | 7 % ~00s
|++++++++ | 14% ~00s
|+++++++++++ | 21% ~00s
|+++++++++++++++ | 29% ~00s
|++++++++++++++++++ | 36% ~00s
|++++++++++++++++++++++ | 43% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|+++++++++++++++++++++++++++++ | 57% ~00s
|+++++++++++++++++++++++++++++++++ | 64% ~00s
|++++++++++++++++++++++++++++++++++++ | 71% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~00s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 7
| | 0 % ~calculating
|+++++ | 9 % ~00s
|++++++++++ | 18% ~00s
|++++++++++++++ | 27% ~00s
|+++++++++++++++++++ | 36% ~00s
|+++++++++++++++++++++++ | 45% ~00s
|++++++++++++++++++++++++++++ | 55% ~00s
|++++++++++++++++++++++++++++++++ | 64% ~00s
|+++++++++++++++++++++++++++++++++++++ | 73% ~00s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 8
| | 0 % ~calculating
|+++++ | 10% ~00s
|++++++++++ | 20% ~00s
|+++++++++++++++ | 30% ~00s
|++++++++++++++++++++ | 40% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++ | 60% ~00s
|+++++++++++++++++++++++++++++++++++ | 70% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 9
| | 0 % ~calculating
|+++++ | 8 % ~00s
|+++++++++ | 17% ~00s
|+++++++++++++ | 25% ~00s
|+++++++++++++++++ | 33% ~00s
|+++++++++++++++++++++ | 42% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++ | 58% ~00s
|++++++++++++++++++++++++++++++++++ | 67% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 10
| | 0 % ~calculating
|+++++ | 10% ~00s
|++++++++++ | 20% ~00s
|+++++++++++++++ | 30% ~00s
|++++++++++++++++++++ | 40% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++ | 60% ~00s
|+++++++++++++++++++++++++++++++++++ | 70% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 11
| | 0 % ~calculating
|+++++++++ | 17% ~00s
|+++++++++++++++++ | 33% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++++++ | 67% ~00s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 1137
Number of edges: 39738
Running Louvain algorithm...
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Maximum modularity in 10 random starts: 0.5666
Number of communities: 12
Elapsed time: 0 seconds
13:03:53 UMAP embedding parameters a = 0.9922 b = 1.112
13:03:53 Read 1137 rows and found 30 numeric columns
13:03:53 Using Annoy for neighbor search, n_neighbors = 30
13:03:53 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
13:03:53 Writing NN index file to temp file /tmp/RtmpfJenoj/file76765488c0a9
13:03:53 Searching Annoy index using 1 thread, search_k = 3000
13:03:53 Annoy recall = 100%
13:03:53 Commencing smooth kNN distance calibration using 1 thread
13:03:54 Initializing from normalized Laplacian + noise
13:03:54 Commencing optimization for 500 epochs, with 43072 positive edges
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
13:03:55 Optimization finished
Calculating cluster 0
| | 0 % ~calculating
|+++++ | 10% ~00s
|++++++++++ | 20% ~00s
|+++++++++++++++ | 30% ~00s
|++++++++++++++++++++ | 40% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++ | 60% ~00s
|+++++++++++++++++++++++++++++++++++ | 70% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 1
| | 0 % ~calculating
|+++++++ | 12% ~00s
|+++++++++++++ | 25% ~00s
|+++++++++++++++++++ | 38% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++++ | 62% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 2
| | 0 % ~calculating
|+++++++ | 12% ~00s
|+++++++++++++ | 25% ~00s
|+++++++++++++++++++ | 38% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++++ | 62% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 3
| | 0 % ~calculating
|++++++++ | 14% ~00s
|+++++++++++++++ | 29% ~00s
|++++++++++++++++++++++ | 43% ~00s
|+++++++++++++++++++++++++++++ | 57% ~00s
|++++++++++++++++++++++++++++++++++++ | 71% ~00s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 4
| | 0 % ~calculating
|+++++ | 10% ~00s
|++++++++++ | 20% ~00s
|+++++++++++++++ | 30% ~00s
|++++++++++++++++++++ | 40% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++ | 60% ~00s
|+++++++++++++++++++++++++++++++++++ | 70% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 5
| | 0 % ~calculating
|+++++++ | 12% ~00s
|+++++++++++++ | 25% ~00s
|+++++++++++++++++++ | 38% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++++ | 62% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 6
| | 0 % ~calculating
|++++ | 7 % ~00s
|++++++++ | 14% ~00s
|+++++++++++ | 21% ~00s
|+++++++++++++++ | 29% ~00s
|++++++++++++++++++ | 36% ~00s
|++++++++++++++++++++++ | 43% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|+++++++++++++++++++++++++++++ | 57% ~00s
|+++++++++++++++++++++++++++++++++ | 64% ~00s
|++++++++++++++++++++++++++++++++++++ | 71% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~00s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 7
| | 0 % ~calculating
|+++++ | 9 % ~00s
|++++++++++ | 18% ~00s
|++++++++++++++ | 27% ~00s
|+++++++++++++++++++ | 36% ~00s
|+++++++++++++++++++++++ | 45% ~00s
|++++++++++++++++++++++++++++ | 55% ~00s
|++++++++++++++++++++++++++++++++ | 64% ~00s
|+++++++++++++++++++++++++++++++++++++ | 73% ~00s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 8
| | 0 % ~calculating
|+++++ | 9 % ~00s
|++++++++++ | 18% ~00s
|++++++++++++++ | 27% ~00s
|+++++++++++++++++++ | 36% ~00s
|+++++++++++++++++++++++ | 45% ~00s
|++++++++++++++++++++++++++++ | 55% ~00s
|++++++++++++++++++++++++++++++++ | 64% ~00s
|+++++++++++++++++++++++++++++++++++++ | 73% ~00s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 9
| | 0 % ~calculating
|+++++ | 10% ~00s
|++++++++++ | 20% ~00s
|+++++++++++++++ | 30% ~00s
|++++++++++++++++++++ | 40% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++ | 60% ~00s
|+++++++++++++++++++++++++++++++++++ | 70% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 10
| | 0 % ~calculating
|+++++ | 8 % ~00s
|+++++++++ | 17% ~00s
|+++++++++++++ | 25% ~00s
|+++++++++++++++++ | 33% ~00s
|+++++++++++++++++++++ | 42% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++ | 58% ~00s
|++++++++++++++++++++++++++++++++++ | 67% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 11
| | 0 % ~calculating
|+++++ | 10% ~00s
|++++++++++ | 20% ~00s
|+++++++++++++++ | 30% ~00s
|++++++++++++++++++++ | 40% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++ | 60% ~00s
|+++++++++++++++++++++++++++++++++++ | 70% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 1137
Number of edges: 39738
Running Louvain algorithm...
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Maximum modularity in 10 random starts: 0.5566
Number of communities: 13
Elapsed time: 0 seconds
13:03:56 UMAP embedding parameters a = 0.9922 b = 1.112
13:03:56 Read 1137 rows and found 30 numeric columns
13:03:56 Using Annoy for neighbor search, n_neighbors = 30
13:03:56 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
13:03:57 Writing NN index file to temp file /tmp/RtmpfJenoj/file76765e0cede1
13:03:57 Searching Annoy index using 1 thread, search_k = 3000
13:03:57 Annoy recall = 100%
13:03:57 Commencing smooth kNN distance calibration using 1 thread
13:03:58 Initializing from normalized Laplacian + noise
13:03:58 Commencing optimization for 500 epochs, with 43072 positive edges
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
13:03:59 Optimization finished
Calculating cluster 0
| | 0 % ~calculating
|++++++ | 11% ~00s
|++++++++++++ | 22% ~00s
|+++++++++++++++++ | 33% ~00s
|+++++++++++++++++++++++ | 44% ~00s
|++++++++++++++++++++++++++++ | 56% ~00s
|++++++++++++++++++++++++++++++++++ | 67% ~00s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 1
| | 0 % ~calculating
|++++++ | 11% ~00s
|++++++++++++ | 22% ~00s
|+++++++++++++++++ | 33% ~00s
|+++++++++++++++++++++++ | 44% ~00s
|++++++++++++++++++++++++++++ | 56% ~00s
|++++++++++++++++++++++++++++++++++ | 67% ~00s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 2
| | 0 % ~calculating
|+++++ | 10% ~00s
|++++++++++ | 20% ~00s
|+++++++++++++++ | 30% ~00s
|++++++++++++++++++++ | 40% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++ | 60% ~00s
|+++++++++++++++++++++++++++++++++++ | 70% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 3
| | 0 % ~calculating
|+++++++ | 12% ~00s
|+++++++++++++ | 25% ~00s
|+++++++++++++++++++ | 38% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++++ | 62% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 4
| | 0 % ~calculating
|+++++++ | 12% ~00s
|+++++++++++++ | 25% ~00s
|+++++++++++++++++++ | 38% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++++ | 62% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 5
| | 0 % ~calculating
|+++++++ | 12% ~00s
|+++++++++++++ | 25% ~00s
|+++++++++++++++++++ | 38% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++++ | 62% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 6
| | 0 % ~calculating
|++++ | 7 % ~00s
|++++++++ | 14% ~00s
|+++++++++++ | 21% ~00s
|+++++++++++++++ | 29% ~00s
|++++++++++++++++++ | 36% ~00s
|++++++++++++++++++++++ | 43% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|+++++++++++++++++++++++++++++ | 57% ~00s
|+++++++++++++++++++++++++++++++++ | 64% ~00s
|++++++++++++++++++++++++++++++++++++ | 71% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~00s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 7
| | 0 % ~calculating
|+++++ | 9 % ~00s
|++++++++++ | 18% ~00s
|++++++++++++++ | 27% ~00s
|+++++++++++++++++++ | 36% ~00s
|+++++++++++++++++++++++ | 45% ~00s
|++++++++++++++++++++++++++++ | 55% ~00s
|++++++++++++++++++++++++++++++++ | 64% ~00s
|+++++++++++++++++++++++++++++++++++++ | 73% ~00s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 8
| | 0 % ~calculating
|+++++ | 10% ~00s
|++++++++++ | 20% ~00s
|+++++++++++++++ | 30% ~00s
|++++++++++++++++++++ | 40% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++ | 60% ~00s
|+++++++++++++++++++++++++++++++++++ | 70% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 9
| | 0 % ~calculating
|+++++ | 8 % ~00s
|+++++++++ | 17% ~00s
|+++++++++++++ | 25% ~00s
|+++++++++++++++++ | 33% ~00s
|+++++++++++++++++++++ | 42% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++ | 58% ~00s
|++++++++++++++++++++++++++++++++++ | 67% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 10
| | 0 % ~calculating
|+++++ | 10% ~00s
|++++++++++ | 20% ~00s
|+++++++++++++++ | 30% ~00s
|++++++++++++++++++++ | 40% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++ | 60% ~00s
|+++++++++++++++++++++++++++++++++++ | 70% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 11
| | 0 % ~calculating
|+++++++++ | 17% ~00s
|+++++++++++++++++ | 33% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++++++ | 67% ~00s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 12
| | 0 % ~calculating
|+++++ | 10% ~00s
|++++++++++ | 20% ~00s
|+++++++++++++++ | 30% ~00s
|++++++++++++++++++++ | 40% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++ | 60% ~00s
|+++++++++++++++++++++++++++++++++++ | 70% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 1137
Number of edges: 39738
Running Louvain algorithm...
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Maximum modularity in 10 random starts: 0.5465
Number of communities: 13
Elapsed time: 0 seconds
13:04:00 UMAP embedding parameters a = 0.9922 b = 1.112
13:04:00 Read 1137 rows and found 30 numeric columns
13:04:00 Using Annoy for neighbor search, n_neighbors = 30
13:04:00 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
13:04:00 Writing NN index file to temp file /tmp/RtmpfJenoj/file76765623ef3bd
13:04:00 Searching Annoy index using 1 thread, search_k = 3000
13:04:01 Annoy recall = 100%
13:04:01 Commencing smooth kNN distance calibration using 1 thread
13:04:02 Initializing from normalized Laplacian + noise
13:04:02 Commencing optimization for 500 epochs, with 43072 positive edges
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
13:04:03 Optimization finished
Calculating cluster 0
| | 0 % ~calculating
|++++++ | 11% ~00s
|++++++++++++ | 22% ~00s
|+++++++++++++++++ | 33% ~00s
|+++++++++++++++++++++++ | 44% ~00s
|++++++++++++++++++++++++++++ | 56% ~00s
|++++++++++++++++++++++++++++++++++ | 67% ~00s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 1
| | 0 % ~calculating
|+++++++ | 12% ~00s
|+++++++++++++ | 25% ~00s
|+++++++++++++++++++ | 38% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++++ | 62% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 2
| | 0 % ~calculating
|+++++++ | 12% ~00s
|+++++++++++++ | 25% ~00s
|+++++++++++++++++++ | 38% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++++ | 62% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 3
| | 0 % ~calculating
|+++++++ | 12% ~00s
|+++++++++++++ | 25% ~00s
|+++++++++++++++++++ | 38% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++++ | 62% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 4
| | 0 % ~calculating
|+++++ | 10% ~00s
|++++++++++ | 20% ~00s
|+++++++++++++++ | 30% ~00s
|++++++++++++++++++++ | 40% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++ | 60% ~00s
|+++++++++++++++++++++++++++++++++++ | 70% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 5
| | 0 % ~calculating
|++++++++ | 14% ~00s
|+++++++++++++++ | 29% ~00s
|++++++++++++++++++++++ | 43% ~00s
|+++++++++++++++++++++++++++++ | 57% ~00s
|++++++++++++++++++++++++++++++++++++ | 71% ~00s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 6
| | 0 % ~calculating
|++++ | 7 % ~00s
|+++++++ | 13% ~00s
|++++++++++ | 20% ~00s
|++++++++++++++ | 27% ~00s
|+++++++++++++++++ | 33% ~00s
|++++++++++++++++++++ | 40% ~00s
|++++++++++++++++++++++++ | 47% ~00s
|+++++++++++++++++++++++++++ | 53% ~00s
|++++++++++++++++++++++++++++++ | 60% ~00s
|++++++++++++++++++++++++++++++++++ | 67% ~00s
|+++++++++++++++++++++++++++++++++++++ | 73% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 7
| | 0 % ~calculating
|+++++ | 9 % ~00s
|++++++++++ | 18% ~00s
|++++++++++++++ | 27% ~00s
|+++++++++++++++++++ | 36% ~00s
|+++++++++++++++++++++++ | 45% ~00s
|++++++++++++++++++++++++++++ | 55% ~00s
|++++++++++++++++++++++++++++++++ | 64% ~00s
|+++++++++++++++++++++++++++++++++++++ | 73% ~00s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 8
| | 0 % ~calculating
|+++++ | 9 % ~00s
|++++++++++ | 18% ~00s
|++++++++++++++ | 27% ~00s
|+++++++++++++++++++ | 36% ~00s
|+++++++++++++++++++++++ | 45% ~00s
|++++++++++++++++++++++++++++ | 55% ~00s
|++++++++++++++++++++++++++++++++ | 64% ~00s
|+++++++++++++++++++++++++++++++++++++ | 73% ~00s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 9
| | 0 % ~calculating
|+++++ | 10% ~00s
|++++++++++ | 20% ~00s
|+++++++++++++++ | 30% ~00s
|++++++++++++++++++++ | 40% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++ | 60% ~00s
|+++++++++++++++++++++++++++++++++++ | 70% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 10
| | 0 % ~calculating
|+++++ | 8 % ~00s
|+++++++++ | 17% ~00s
|+++++++++++++ | 25% ~00s
|+++++++++++++++++ | 33% ~00s
|+++++++++++++++++++++ | 42% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++ | 58% ~00s
|++++++++++++++++++++++++++++++++++ | 67% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 11
| | 0 % ~calculating
|+++++ | 10% ~00s
|++++++++++ | 20% ~00s
|+++++++++++++++ | 30% ~00s
|++++++++++++++++++++ | 40% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++ | 60% ~00s
|+++++++++++++++++++++++++++++++++++ | 70% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 12
| | 0 % ~calculating
|+++++ | 9 % ~00s
|++++++++++ | 18% ~00s
|++++++++++++++ | 27% ~00s
|+++++++++++++++++++ | 36% ~00s
|+++++++++++++++++++++++ | 45% ~00s
|++++++++++++++++++++++++++++ | 55% ~00s
|++++++++++++++++++++++++++++++++ | 64% ~00s
|+++++++++++++++++++++++++++++++++++++ | 73% ~00s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 1137
Number of edges: 39738
Running Louvain algorithm...
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Maximum modularity in 10 random starts: 0.5364
Number of communities: 14
Elapsed time: 0 seconds
13:04:04 UMAP embedding parameters a = 0.9922 b = 1.112
13:04:04 Read 1137 rows and found 30 numeric columns
13:04:04 Using Annoy for neighbor search, n_neighbors = 30
13:04:04 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
13:04:04 Writing NN index file to temp file /tmp/RtmpfJenoj/file767657373bc63
13:04:04 Searching Annoy index using 1 thread, search_k = 3000
13:04:05 Annoy recall = 100%
13:04:05 Commencing smooth kNN distance calibration using 1 thread
13:04:06 Initializing from normalized Laplacian + noise
13:04:06 Commencing optimization for 500 epochs, with 43072 positive edges
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
13:04:07 Optimization finished
Calculating cluster 0
| | 0 % ~calculating
|+++++++ | 12% ~00s
|+++++++++++++ | 25% ~00s
|+++++++++++++++++++ | 38% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++++ | 62% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 1
| | 0 % ~calculating
|+++++ | 10% ~00s
|++++++++++ | 20% ~00s
|+++++++++++++++ | 30% ~00s
|++++++++++++++++++++ | 40% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++ | 60% ~00s
|+++++++++++++++++++++++++++++++++++ | 70% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 2
| | 0 % ~calculating
|+++++++ | 12% ~00s
|+++++++++++++ | 25% ~00s
|+++++++++++++++++++ | 38% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++++ | 62% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 3
| | 0 % ~calculating
|+++++ | 10% ~00s
|++++++++++ | 20% ~00s
|+++++++++++++++ | 30% ~00s
|++++++++++++++++++++ | 40% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++ | 60% ~00s
|+++++++++++++++++++++++++++++++++++ | 70% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 4
| | 0 % ~calculating
|+++++++ | 12% ~00s
|+++++++++++++ | 25% ~00s
|+++++++++++++++++++ | 38% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++++ | 62% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 5
| | 0 % ~calculating
|++++++++ | 14% ~00s
|+++++++++++++++ | 29% ~00s
|++++++++++++++++++++++ | 43% ~00s
|+++++++++++++++++++++++++++++ | 57% ~00s
|++++++++++++++++++++++++++++++++++++ | 71% ~00s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 6
| | 0 % ~calculating
|++++ | 7 % ~00s
|+++++++ | 13% ~00s
|++++++++++ | 20% ~00s
|++++++++++++++ | 27% ~00s
|+++++++++++++++++ | 33% ~00s
|++++++++++++++++++++ | 40% ~00s
|++++++++++++++++++++++++ | 47% ~00s
|+++++++++++++++++++++++++++ | 53% ~00s
|++++++++++++++++++++++++++++++ | 60% ~00s
|++++++++++++++++++++++++++++++++++ | 67% ~00s
|+++++++++++++++++++++++++++++++++++++ | 73% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 7
| | 0 % ~calculating
|+++++ | 9 % ~00s
|++++++++++ | 18% ~00s
|++++++++++++++ | 27% ~00s
|+++++++++++++++++++ | 36% ~00s
|+++++++++++++++++++++++ | 45% ~00s
|++++++++++++++++++++++++++++ | 55% ~00s
|++++++++++++++++++++++++++++++++ | 64% ~00s
|+++++++++++++++++++++++++++++++++++++ | 73% ~00s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 8
| | 0 % ~calculating
|+++++ | 9 % ~00s
|++++++++++ | 18% ~00s
|++++++++++++++ | 27% ~00s
|+++++++++++++++++++ | 36% ~00s
|+++++++++++++++++++++++ | 45% ~00s
|++++++++++++++++++++++++++++ | 55% ~00s
|++++++++++++++++++++++++++++++++ | 64% ~00s
|+++++++++++++++++++++++++++++++++++++ | 73% ~00s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 9
| | 0 % ~calculating
|+++++ | 10% ~00s
|++++++++++ | 20% ~00s
|+++++++++++++++ | 30% ~00s
|++++++++++++++++++++ | 40% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++ | 60% ~00s
|+++++++++++++++++++++++++++++++++++ | 70% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 10
| | 0 % ~calculating
|+++++ | 8 % ~00s
|+++++++++ | 17% ~00s
|+++++++++++++ | 25% ~00s
|+++++++++++++++++ | 33% ~00s
|+++++++++++++++++++++ | 42% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++ | 58% ~00s
|++++++++++++++++++++++++++++++++++ | 67% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 11
| | 0 % ~calculating
|+++++ | 10% ~00s
|++++++++++ | 20% ~00s
|+++++++++++++++ | 30% ~00s
|++++++++++++++++++++ | 40% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++ | 60% ~00s
|+++++++++++++++++++++++++++++++++++ | 70% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 12
| | 0 % ~calculating
|+++++++++++++ | 25% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 13
| | 0 % ~calculating
|+++++ | 10% ~00s
|++++++++++ | 20% ~00s
|+++++++++++++++ | 30% ~00s
|++++++++++++++++++++ | 40% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++ | 60% ~00s
|+++++++++++++++++++++++++++++++++++ | 70% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 1137
Number of edges: 39738
Running Louvain algorithm...
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Maximum modularity in 10 random starts: 0.5275
Number of communities: 15
Elapsed time: 0 seconds
13:04:08 UMAP embedding parameters a = 0.9922 b = 1.112
13:04:08 Read 1137 rows and found 30 numeric columns
13:04:08 Using Annoy for neighbor search, n_neighbors = 30
13:04:08 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
13:04:08 Writing NN index file to temp file /tmp/RtmpfJenoj/file767655f8a538b
13:04:08 Searching Annoy index using 1 thread, search_k = 3000
13:04:08 Annoy recall = 100%
13:04:09 Commencing smooth kNN distance calibration using 1 thread
13:04:10 Initializing from normalized Laplacian + noise
13:04:10 Commencing optimization for 500 epochs, with 43072 positive edges
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
13:04:11 Optimization finished
Calculating cluster 0
| | 0 % ~calculating
|+++++++ | 12% ~00s
|+++++++++++++ | 25% ~00s
|+++++++++++++++++++ | 38% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++++ | 62% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 1
| | 0 % ~calculating
|+++++ | 10% ~00s
|++++++++++ | 20% ~00s
|+++++++++++++++ | 30% ~00s
|++++++++++++++++++++ | 40% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++ | 60% ~00s
|+++++++++++++++++++++++++++++++++++ | 70% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 2
| | 0 % ~calculating
|+++++++ | 12% ~00s
|+++++++++++++ | 25% ~00s
|+++++++++++++++++++ | 38% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++++ | 62% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 3
| | 0 % ~calculating
|+++++ | 10% ~00s
|++++++++++ | 20% ~00s
|+++++++++++++++ | 30% ~00s
|++++++++++++++++++++ | 40% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++ | 60% ~00s
|+++++++++++++++++++++++++++++++++++ | 70% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 4
| | 0 % ~calculating
|+++++++ | 12% ~00s
|+++++++++++++ | 25% ~00s
|+++++++++++++++++++ | 38% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++++ | 62% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 5
| | 0 % ~calculating
|++++++++ | 14% ~00s
|+++++++++++++++ | 29% ~00s
|++++++++++++++++++++++ | 43% ~00s
|+++++++++++++++++++++++++++++ | 57% ~00s
|++++++++++++++++++++++++++++++++++++ | 71% ~00s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 6
| | 0 % ~calculating
|+++++ | 9 % ~00s
|++++++++++ | 18% ~00s
|++++++++++++++ | 27% ~00s
|+++++++++++++++++++ | 36% ~00s
|+++++++++++++++++++++++ | 45% ~00s
|++++++++++++++++++++++++++++ | 55% ~00s
|++++++++++++++++++++++++++++++++ | 64% ~00s
|+++++++++++++++++++++++++++++++++++++ | 73% ~00s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 7
| | 0 % ~calculating
|+++++ | 9 % ~00s
|++++++++++ | 18% ~00s
|++++++++++++++ | 27% ~00s
|+++++++++++++++++++ | 36% ~00s
|+++++++++++++++++++++++ | 45% ~00s
|++++++++++++++++++++++++++++ | 55% ~00s
|++++++++++++++++++++++++++++++++ | 64% ~00s
|+++++++++++++++++++++++++++++++++++++ | 73% ~00s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 8
| | 0 % ~calculating
|+++++ | 10% ~00s
|++++++++++ | 20% ~00s
|+++++++++++++++ | 30% ~00s
|++++++++++++++++++++ | 40% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++ | 60% ~00s
|+++++++++++++++++++++++++++++++++++ | 70% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 9
| | 0 % ~calculating
|++++ | 6 % ~00s
|+++++++ | 12% ~00s
|++++++++++ | 19% ~00s
|+++++++++++++ | 25% ~00s
|++++++++++++++++ | 31% ~00s
|+++++++++++++++++++ | 38% ~00s
|++++++++++++++++++++++ | 44% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|+++++++++++++++++++++++++++++ | 56% ~00s
|++++++++++++++++++++++++++++++++ | 62% ~00s
|+++++++++++++++++++++++++++++++++++ | 69% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 10
| | 0 % ~calculating
|+++++ | 8 % ~00s
|+++++++++ | 17% ~00s
|+++++++++++++ | 25% ~00s
|+++++++++++++++++ | 33% ~00s
|+++++++++++++++++++++ | 42% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++ | 58% ~00s
|++++++++++++++++++++++++++++++++++ | 67% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 11
| | 0 % ~calculating
|+++++ | 10% ~00s
|++++++++++ | 20% ~00s
|+++++++++++++++ | 30% ~00s
|++++++++++++++++++++ | 40% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++ | 60% ~00s
|+++++++++++++++++++++++++++++++++++ | 70% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 12
| | 0 % ~calculating
|+++++ | 8 % ~00s
|+++++++++ | 17% ~00s
|+++++++++++++ | 25% ~00s
|+++++++++++++++++ | 33% ~00s
|+++++++++++++++++++++ | 42% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++ | 58% ~00s
|++++++++++++++++++++++++++++++++++ | 67% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 13
| | 0 % ~calculating
|++++++++++ | 20% ~00s
|++++++++++++++++++++ | 40% ~00s
|++++++++++++++++++++++++++++++ | 60% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 14
| | 0 % ~calculating
|++++++ | 11% ~00s
|++++++++++++ | 22% ~00s
|+++++++++++++++++ | 33% ~00s
|+++++++++++++++++++++++ | 44% ~00s
|++++++++++++++++++++++++++++ | 56% ~00s
|++++++++++++++++++++++++++++++++++ | 67% ~00s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 1137
Number of edges: 39738
Running Louvain algorithm...
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Maximum modularity in 10 random starts: 0.5186
Number of communities: 16
Elapsed time: 0 seconds
13:04:12 UMAP embedding parameters a = 0.9922 b = 1.112
13:04:12 Read 1137 rows and found 30 numeric columns
13:04:12 Using Annoy for neighbor search, n_neighbors = 30
13:04:12 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
13:04:12 Writing NN index file to temp file /tmp/RtmpfJenoj/file767656782cb10
13:04:12 Searching Annoy index using 1 thread, search_k = 3000
13:04:12 Annoy recall = 100%
13:04:13 Commencing smooth kNN distance calibration using 1 thread
13:04:14 Initializing from normalized Laplacian + noise
13:04:14 Commencing optimization for 500 epochs, with 43072 positive edges
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
13:04:15 Optimization finished
Calculating cluster 0
| | 0 % ~calculating
|+++++++ | 12% ~00s
|+++++++++++++ | 25% ~00s
|+++++++++++++++++++ | 38% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++++ | 62% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 1
| | 0 % ~calculating
|+++++ | 10% ~00s
|++++++++++ | 20% ~00s
|+++++++++++++++ | 30% ~00s
|++++++++++++++++++++ | 40% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++ | 60% ~00s
|+++++++++++++++++++++++++++++++++++ | 70% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 2
| | 0 % ~calculating
|+++++++ | 12% ~00s
|+++++++++++++ | 25% ~00s
|+++++++++++++++++++ | 38% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++++ | 62% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 3
| | 0 % ~calculating
|+++++++ | 12% ~00s
|+++++++++++++ | 25% ~00s
|+++++++++++++++++++ | 38% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++++ | 62% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 4
| | 0 % ~calculating
|+++++ | 10% ~00s
|++++++++++ | 20% ~00s
|+++++++++++++++ | 30% ~00s
|++++++++++++++++++++ | 40% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++ | 60% ~00s
|+++++++++++++++++++++++++++++++++++ | 70% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 5
| | 0 % ~calculating
|++++++++ | 14% ~00s
|+++++++++++++++ | 29% ~00s
|++++++++++++++++++++++ | 43% ~00s
|+++++++++++++++++++++++++++++ | 57% ~00s
|++++++++++++++++++++++++++++++++++++ | 71% ~00s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 6
| | 0 % ~calculating
|+++++ | 8 % ~00s
|+++++++++ | 17% ~00s
|+++++++++++++ | 25% ~00s
|+++++++++++++++++ | 33% ~00s
|+++++++++++++++++++++ | 42% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++ | 58% ~00s
|++++++++++++++++++++++++++++++++++ | 67% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 7
| | 0 % ~calculating
|+++++ | 9 % ~00s
|++++++++++ | 18% ~00s
|++++++++++++++ | 27% ~00s
|+++++++++++++++++++ | 36% ~00s
|+++++++++++++++++++++++ | 45% ~00s
|++++++++++++++++++++++++++++ | 55% ~00s
|++++++++++++++++++++++++++++++++ | 64% ~00s
|+++++++++++++++++++++++++++++++++++++ | 73% ~00s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 8
| | 0 % ~calculating
|++++ | 6 % ~00s
|+++++++ | 12% ~00s
|++++++++++ | 19% ~00s
|+++++++++++++ | 25% ~00s
|++++++++++++++++ | 31% ~00s
|+++++++++++++++++++ | 38% ~00s
|++++++++++++++++++++++ | 44% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|+++++++++++++++++++++++++++++ | 56% ~00s
|++++++++++++++++++++++++++++++++ | 62% ~00s
|+++++++++++++++++++++++++++++++++++ | 69% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 9
| | 0 % ~calculating
|+++++ | 8 % ~00s
|+++++++++ | 17% ~00s
|+++++++++++++ | 25% ~00s
|+++++++++++++++++ | 33% ~00s
|+++++++++++++++++++++ | 42% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++ | 58% ~00s
|++++++++++++++++++++++++++++++++++ | 67% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 10
| | 0 % ~calculating
|+++++ | 10% ~00s
|++++++++++ | 20% ~00s
|+++++++++++++++ | 30% ~00s
|++++++++++++++++++++ | 40% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++ | 60% ~00s
|+++++++++++++++++++++++++++++++++++ | 70% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 11
| | 0 % ~calculating
|+++++ | 9 % ~00s
|++++++++++ | 18% ~00s
|++++++++++++++ | 27% ~00s
|+++++++++++++++++++ | 36% ~00s
|+++++++++++++++++++++++ | 45% ~00s
|++++++++++++++++++++++++++++ | 55% ~00s
|++++++++++++++++++++++++++++++++ | 64% ~00s
|+++++++++++++++++++++++++++++++++++++ | 73% ~00s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 12
| | 0 % ~calculating
|+++++ | 8 % ~00s
|+++++++++ | 17% ~00s
|+++++++++++++ | 25% ~00s
|+++++++++++++++++ | 33% ~00s
|+++++++++++++++++++++ | 42% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++ | 58% ~00s
|++++++++++++++++++++++++++++++++++ | 67% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 13
| | 0 % ~calculating
|+++++ | 8 % ~00s
|+++++++++ | 17% ~00s
|+++++++++++++ | 25% ~00s
|+++++++++++++++++ | 33% ~00s
|+++++++++++++++++++++ | 42% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++ | 58% ~00s
|++++++++++++++++++++++++++++++++++ | 67% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 14
| | 0 % ~calculating
|++++++++++ | 20% ~00s
|++++++++++++++++++++ | 40% ~00s
|++++++++++++++++++++++++++++++ | 60% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 15
| | 0 % ~calculating
|++++++ | 11% ~00s
|++++++++++++ | 22% ~00s
|+++++++++++++++++ | 33% ~00s
|+++++++++++++++++++++++ | 44% ~00s
|++++++++++++++++++++++++++++ | 56% ~00s
|++++++++++++++++++++++++++++++++++ | 67% ~00s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 1137
Number of edges: 39738
Running Louvain algorithm...
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Maximum modularity in 10 random starts: 0.5097
Number of communities: 16
Elapsed time: 0 seconds
13:04:16 UMAP embedding parameters a = 0.9922 b = 1.112
13:04:16 Read 1137 rows and found 30 numeric columns
13:04:16 Using Annoy for neighbor search, n_neighbors = 30
13:04:16 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
13:04:16 Writing NN index file to temp file /tmp/RtmpfJenoj/file7676567fec782
13:04:16 Searching Annoy index using 1 thread, search_k = 3000
13:04:17 Annoy recall = 100%
13:04:17 Commencing smooth kNN distance calibration using 1 thread
13:04:18 Initializing from normalized Laplacian + noise
13:04:18 Commencing optimization for 500 epochs, with 43072 positive edges
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
13:04:19 Optimization finished
Calculating cluster 0
| | 0 % ~calculating
|+++++++ | 12% ~00s
|+++++++++++++ | 25% ~00s
|+++++++++++++++++++ | 38% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++++ | 62% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 1
| | 0 % ~calculating
|+++++ | 10% ~00s
|++++++++++ | 20% ~00s
|+++++++++++++++ | 30% ~00s
|++++++++++++++++++++ | 40% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++ | 60% ~00s
|+++++++++++++++++++++++++++++++++++ | 70% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 2
| | 0 % ~calculating
|+++++ | 10% ~00s
|++++++++++ | 20% ~00s
|+++++++++++++++ | 30% ~00s
|++++++++++++++++++++ | 40% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++ | 60% ~00s
|+++++++++++++++++++++++++++++++++++ | 70% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 3
| | 0 % ~calculating
|+++++++ | 12% ~00s
|+++++++++++++ | 25% ~00s
|+++++++++++++++++++ | 38% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++++ | 62% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 4
| | 0 % ~calculating
|+++++++ | 12% ~00s
|+++++++++++++ | 25% ~00s
|+++++++++++++++++++ | 38% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++++ | 62% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 5
| | 0 % ~calculating
|++++++++ | 14% ~00s
|+++++++++++++++ | 29% ~00s
|++++++++++++++++++++++ | 43% ~00s
|+++++++++++++++++++++++++++++ | 57% ~00s
|++++++++++++++++++++++++++++++++++++ | 71% ~00s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 6
| | 0 % ~calculating
|+++++ | 9 % ~00s
|++++++++++ | 18% ~00s
|++++++++++++++ | 27% ~00s
|+++++++++++++++++++ | 36% ~00s
|+++++++++++++++++++++++ | 45% ~00s
|++++++++++++++++++++++++++++ | 55% ~00s
|++++++++++++++++++++++++++++++++ | 64% ~00s
|+++++++++++++++++++++++++++++++++++++ | 73% ~00s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 7
| | 0 % ~calculating
|+++++ | 9 % ~00s
|++++++++++ | 18% ~00s
|++++++++++++++ | 27% ~00s
|+++++++++++++++++++ | 36% ~00s
|+++++++++++++++++++++++ | 45% ~00s
|++++++++++++++++++++++++++++ | 55% ~00s
|++++++++++++++++++++++++++++++++ | 64% ~00s
|+++++++++++++++++++++++++++++++++++++ | 73% ~00s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 8
| | 0 % ~calculating
|++++ | 6 % ~00s
|+++++++ | 12% ~00s
|++++++++++ | 19% ~00s
|+++++++++++++ | 25% ~00s
|++++++++++++++++ | 31% ~00s
|+++++++++++++++++++ | 38% ~00s
|++++++++++++++++++++++ | 44% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|+++++++++++++++++++++++++++++ | 56% ~00s
|++++++++++++++++++++++++++++++++ | 62% ~00s
|+++++++++++++++++++++++++++++++++++ | 69% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 9
| | 0 % ~calculating
|+++++ | 8 % ~00s
|+++++++++ | 17% ~00s
|+++++++++++++ | 25% ~00s
|+++++++++++++++++ | 33% ~00s
|+++++++++++++++++++++ | 42% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++ | 58% ~00s
|++++++++++++++++++++++++++++++++++ | 67% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 10
| | 0 % ~calculating
|++++++ | 11% ~00s
|++++++++++++ | 22% ~00s
|+++++++++++++++++ | 33% ~00s
|+++++++++++++++++++++++ | 44% ~00s
|++++++++++++++++++++++++++++ | 56% ~00s
|++++++++++++++++++++++++++++++++++ | 67% ~00s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 11
| | 0 % ~calculating
|+++++ | 9 % ~00s
|++++++++++ | 18% ~00s
|++++++++++++++ | 27% ~00s
|+++++++++++++++++++ | 36% ~00s
|+++++++++++++++++++++++ | 45% ~00s
|++++++++++++++++++++++++++++ | 55% ~00s
|++++++++++++++++++++++++++++++++ | 64% ~00s
|+++++++++++++++++++++++++++++++++++++ | 73% ~00s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 12
| | 0 % ~calculating
|+++++ | 8 % ~00s
|+++++++++ | 17% ~00s
|+++++++++++++ | 25% ~00s
|+++++++++++++++++ | 33% ~00s
|+++++++++++++++++++++ | 42% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++ | 58% ~00s
|++++++++++++++++++++++++++++++++++ | 67% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 13
| | 0 % ~calculating
|+++++ | 10% ~00s
|++++++++++ | 20% ~00s
|+++++++++++++++ | 30% ~00s
|++++++++++++++++++++ | 40% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++ | 60% ~00s
|+++++++++++++++++++++++++++++++++++ | 70% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 14
| | 0 % ~calculating
|+++++ | 8 % ~00s
|+++++++++ | 17% ~00s
|+++++++++++++ | 25% ~00s
|+++++++++++++++++ | 33% ~00s
|+++++++++++++++++++++ | 42% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++ | 58% ~00s
|++++++++++++++++++++++++++++++++++ | 67% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 15
| | 0 % ~calculating
|++++++++++ | 20% ~00s
|++++++++++++++++++++ | 40% ~00s
|++++++++++++++++++++++++++++++ | 60% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
marrangeGrob(plots, nrow=2, ncol=2)
ml <- marrangeGrob(plots, nrow=2, ncol=2)
ggsave(filename = 'gad1_sequential_clustering_umaps.pdf', path = file.path(output_dir_plot, '20220721_1'), ml)
Saving 7.29 x 4.5 in image
genes = rownames(jy_gad)
plots <- lapply(1:length(genes), function(i){
plot_features_umap(jy_gad, genes[i], pt.size = 0.5)
})
umaps = plot_grid(plotlist = plots, label_size = 10, nrow = 4)
umaps
#ggsave(plot = umaps, filename = 'test_immature_umapl_expr_plots_size1_alpha1.png', path = file.path(output_dir_plot, '20220721_1'), width = 10, height = 4, dpi = 150)
big_dimplot <- function(sobj, grep_pattern){
dp = DimPlot(sobj, cells.highlight = list(imp = which(grepl(grep_pattern, sobj$area))))
dp <- dp + scale_color_manual(values = c('grey90', '#1982c4'), labels=c('other', grep_pattern)) + NoAxes()
}
patterns = c('408_CC', '408_MS_CC', 'vMS_TC', 'dMS_TC', '408_TC', '164_CC', '164_MS_CC', '164_MS_TC', '164_TC')
plots <- lapply(1:length(patterns), function(i){
big_dimplot(jy_gad, grep_pattern = patterns[i])
})
Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.
Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.
Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.
Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.
Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.
Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.
Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.
Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.
Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.
areas = plot_grid(plotlist = plots, label_size = 10, nrow = 3)
areas
ggsave(plot = areas, filename = 'test_gad_area_plots_size1_alpha1.png', path = file.path(output_dir_plot, '20220721_1'), width = 14, height = 8, dpi = 150)
plot_vertical_smoothed <- function(sobj, gene, images_ordered){
expmat <- as.matrix(FetchData(sobj, gene))
## What's the logic. I need to build a rectangle, so just give it the x, y
## and fill is not density
xpos = rep(0, length(images_ordered))
ypos = 1:length(images_ordered)
image_expr = c()
for(i in 1:length(images_ordered)){
image = images_ordered[i]
gene_expr_in_subset = expmat[sobj$IMAGE.NAME == image]
image_expr = c(image_expr, mean(gene_expr_in_subset))
}
tile_df = as.data.frame(cbind(xpos, ypos, image_expr))
colnames(tile_df) <- c('X', 'Y','mean_expr')
p <- tile_df %>%
ggplot(aes(x = X, y = Y, fill = mean_expr)) + geom_raster(interpolate = TRUE) +
coord_fixed() + theme_classic() + NoAxes() + NoLegend() + ggtitle(gene) + theme(title = element_text(face = 'bold', size = rel(0.5), hjust = 1))
return(p)
}
plot_vertical_smoothed(jy_164, 'PAX6', images_ordered)
genes_to_show = c('DCX', 'GAD1', 'DLX2', 'NKX2.1', 'PROX1', 'MAF1', 'TSHZ1', 'CXCR4', 'CXCL12', 'CALB2', 'TBR1')
genes = genes_to_show
plots <- lapply(1:length(genes), function(i){
plot_vertical_smoothed(jy_164, genes[i], images_ordered)
})
verts= plot_grid(plotlist = plots, label_size = 10, nrow = 1)
verts
#ggsave(plot = verts, filename = 'test_408_smoothed-vertical_expr_plots_size1_alpha1.png', path = file.path(output_dir_plot, '20220721_1'), width = 18, height = 8, dpi = 150)
rplot <- RidgePlot(jy_all, features = genes_to_show, group.by = 'area', ncol = 2)
ggsave(rplot, filename = 'rplot_by_area.png', path = file.path(output_dir_plot, '20220727_1'), width = 6, height = 15, dpi = 150)
Picking joint bandwidth of 0.283
Picking joint bandwidth of 1.05
Picking joint bandwidth of 0.896
Picking joint bandwidth of 1.14
Picking joint bandwidth of 0.774
Picking joint bandwidth of 0.472
Picking joint bandwidth of 0.845
Picking joint bandwidth of 0.918
Picking joint bandwidth of 1.02
Picking joint bandwidth of 0.973
Picking joint bandwidth of 0.955
vplot <- VlnPlot(jy_all, features = genes_to_show, group.by = 'area', ncol = 3)
vplot
ggsave(vplot, filename = 'vplot_by_area.png', path = file.path(output_dir_plot, '20220727_1'), width = 10, height = 15, dpi = 150)
vplot <- VlnPlot(jy_all, features = genes_to_show, group.by = 'area', ncol = 3)
vplot
ggsave(vplot, filename = 'vplot_by_area.png', path = file.path(output_dir_plot, '20220727_1'), width = 10, height = 15, dpi = 150)
genes_to_show = c('DCX', 'GAD1', 'CXCR4', 'DLX2', 'NKX2.1', 'PROX1', 'MAF1', 'TSHZ1', 'CXCL12', 'CALB2', 'TBR1', 'COUPTF2', 'SP8', 'SCGN')
dplot <- DotPlot(jy_all, features = genes_to_show, group.by = 'broad_areas',) + RotatedAxis() + coord_flip()
dplot
#ggsave(dplot, filename = 'dplot_by_broadarea_allgenes.png', path = file.path(output_dir_plot, '20220727_1'), width = 9, height = 3, dpi = 150)
broad_areas = c()
jy_all$broad_areas = 'wrong'
jy_all$broad_areas[grepl('408', jy_all$area) & grepl('TC', jy_all$area)] = 'pVentral'
jy_all$broad_areas[grepl('408', jy_all$area) & grepl('CC', jy_all$area)] = 'pDorsal'
jy_all$broad_areas[grepl('164', jy_all$area) & grepl('TC', jy_all$area)] = 'aVentral'
jy_all$broad_areas[grepl('164', jy_all$area) & grepl('CC', jy_all$area)] = 'aDorsal'
big_dimplot <- function(sobj, grep_pattern){
dp = DimPlot(sobj, cells.highlight = list(imp = which(grepl(grep_pattern, sobj$broad_areas))))
dp <- dp + scale_color_manual(values = c('grey90', '#1982c4'), labels=c('other', grep_pattern)) + NoAxes()
}
patterns = c('AntDrs', 'AntVnt', 'PosDrs', 'PosVnt')
plots <- lapply(1:length(patterns), function(i){
big_dimplot(jy_all, grep_pattern = patterns[i])
})
Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.
Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.
Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.
Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.
areas = plot_grid(plotlist = plots, label_size = 10, nrow = 3)
areas
ggsave(plot = areas, filename = 'umap_broad_areas.png', path = file.path(output_dir_plot, '20220727_1'), width = 10, height = 8, dpi = 150)